EDA
MJ
2019 8 17
Library & Data Import
# rm(list=ls())
# setwd("/Users/exntu/ieee-fraud-detection")
options(scipen=999)
library(dplyr)
library(mice)
library(corrplot)
library(ggplot2)
library(caret)
train_i <- read.csv("train_identity.csv", na.strings=c('',NA))
test_i <- read.csv("test_identity.csv", na.strings=c('',NA))
train_t <- read.csv("train_transaction.csv", na.strings=c('',NA))
test_t <- read.csv("test_transaction.csv", na.strings=c('',NA))1. Import Data
train_i
str(train_i)'data.frame': 144233 obs. of 41 variables:
$ TransactionID: int 2987004 2987008 2987010 2987011 2987016 2987017 2987022 2987038 2987040 2987048 ...
$ id_01 : num 0 -5 -5 -5 0 -5 -15 0 -10 -5 ...
$ id_02 : num 70787 98945 191631 221832 7460 ...
$ id_03 : num NA NA 0 NA 0 3 NA 0 0 NA ...
$ id_04 : num NA NA 0 NA 0 0 NA 0 0 NA ...
$ id_05 : num NA 0 0 0 1 3 NA 0 0 0 ...
$ id_06 : num NA -5 0 -6 0 0 NA -10 0 0 ...
$ id_07 : num NA NA NA NA NA NA NA NA NA NA ...
$ id_08 : num NA NA NA NA NA NA NA NA NA NA ...
$ id_09 : num NA NA 0 NA 0 3 NA 0 0 NA ...
$ id_10 : num NA NA 0 NA 0 0 NA 0 0 NA ...
$ id_11 : num 100 100 100 100 100 100 NA 100 100 100 ...
$ id_12 : Factor w/ 2 levels "Found","NotFound": 2 2 2 2 2 2 2 1 2 2 ...
$ id_13 : num NA 49 52 52 NA 52 14 NA 52 52 ...
$ id_14 : num -480 -300 NA NA -300 -300 NA -300 NA NA ...
$ id_15 : Factor w/ 3 levels "Found","New",..: 2 2 1 2 1 1 NA 1 1 2 ...
$ id_16 : Factor w/ 2 levels "Found","NotFound": 2 2 1 2 1 1 NA 1 1 2 ...
$ id_17 : num 166 166 121 225 166 166 NA 166 121 225 ...
$ id_18 : num NA NA NA NA 15 18 NA 15 NA NA ...
$ id_19 : num 542 621 410 176 529 529 NA 352 410 484 ...
$ id_20 : num 144 500 142 507 575 600 NA 533 142 507 ...
$ id_21 : num NA NA NA NA NA NA NA NA NA NA ...
$ id_22 : num NA NA NA NA NA NA NA NA NA NA ...
$ id_23 : Factor w/ 3 levels "IP_PROXY:ANONYMOUS",..: NA NA NA NA NA NA NA NA NA NA ...
$ id_24 : num NA NA NA NA NA NA NA NA NA NA ...
$ id_25 : num NA NA NA NA NA NA NA NA NA NA ...
$ id_26 : num NA NA NA NA NA NA NA NA NA NA ...
$ id_27 : Factor w/ 2 levels "Found","NotFound": NA NA NA NA NA NA NA NA NA NA ...
$ id_28 : Factor w/ 2 levels "Found","New": 2 2 1 2 1 1 NA 1 1 2 ...
$ id_29 : Factor w/ 2 levels "Found","NotFound": 2 2 1 2 1 1 NA 1 1 2 ...
$ id_30 : Factor w/ 75 levels "Android","Android 4.4.2",..: 8 28 NA NA 45 70 NA 1 NA NA ...
$ id_31 : Factor w/ 130 levels "android","android browser 4.0",..: 120 93 32 32 32 32 NA 32 32 32 ...
$ id_32 : num 32 32 NA NA 24 24 NA 32 NA NA ...
$ id_33 : Factor w/ 260 levels "0x0","1023x767",..: 165 49 NA NA 41 61 NA 133 NA NA ...
$ id_34 : Factor w/ 4 levels "match_status:-1",..: 4 3 NA NA 4 4 NA 4 NA NA ...
$ id_35 : logi TRUE TRUE FALSE FALSE TRUE TRUE ...
$ id_36 : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ id_37 : logi TRUE FALSE TRUE TRUE TRUE TRUE ...
$ id_38 : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
$ DeviceType : Factor w/ 2 levels "desktop","mobile": 2 2 1 1 1 1 NA 2 1 1 ...
$ DeviceInfo : Factor w/ 1786 levels "0PAJ5","0PJA2",..: 1009 469 1663 NA 723 1663 NA NA 1663 1663 ...summary(train_i) TransactionID id_01 id_02 id_03
Min. :2987004 Min. :-100.00 Min. : 1 Min. :-13.00
1st Qu.:3077142 1st Qu.: -10.00 1st Qu.: 67992 1st Qu.: 0.00
Median :3198818 Median : -5.00 Median :125800 Median : 0.00
Mean :3236329 Mean : -10.17 Mean :174717 Mean : 0.06
3rd Qu.:3392923 3rd Qu.: -5.00 3rd Qu.:228749 3rd Qu.: 0.00
Max. :3577534 Max. : 0.00 Max. :999595 Max. : 10.00
NA's :3361 NA's :77909
id_04 id_05 id_06 id_07
Min. :-28.00 Min. :-72.000 Min. :-100.000 Min. :-46.00
1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: -6.000 1st Qu.: 5.00
Median : 0.00 Median : 0.000 Median : 0.000 Median : 14.00
Mean : -0.06 Mean : 1.616 Mean : -6.699 Mean : 13.29
3rd Qu.: 0.00 3rd Qu.: 1.000 3rd Qu.: 0.000 3rd Qu.: 22.00
Max. : 0.00 Max. : 52.000 Max. : 0.000 Max. : 61.00
NA's :77909 NA's :7368 NA's :7368 NA's :139078
id_08 id_09 id_10 id_11
Min. :-100.0 Min. :-36.00 Min. :-100.0 Min. : 90.00
1st Qu.: -48.0 1st Qu.: 0.00 1st Qu.: 0.0 1st Qu.:100.00
Median : -34.0 Median : 0.00 Median : 0.0 Median :100.00
Mean : -38.6 Mean : 0.09 Mean : -0.3 Mean : 99.75
3rd Qu.: -23.0 3rd Qu.: 0.00 3rd Qu.: 0.0 3rd Qu.:100.00
Max. : 0.0 Max. : 25.00 Max. : 0.0 Max. :100.00
NA's :139078 NA's :69307 NA's :69307 NA's :3255
id_12 id_13 id_14 id_15
Found : 21208 Min. :10.00 Min. :-660.0 Found :67728
NotFound:123025 1st Qu.:49.00 1st Qu.:-360.0 New :61612
Median :52.00 Median :-300.0 Unknown:11645
Mean :48.05 Mean :-344.5 NA's : 3248
3rd Qu.:52.00 3rd Qu.:-300.0
Max. :64.00 Max. : 720.0
NA's :16913 NA's :64189
id_16 id_17 id_18 id_19
Found :66324 Min. :100.0 Min. :10.00 Min. :100.0
NotFound:63016 1st Qu.:166.0 1st Qu.:13.00 1st Qu.:266.0
NA's :14893 Median :166.0 Median :15.00 Median :341.0
Mean :189.5 Mean :14.24 Mean :353.1
3rd Qu.:225.0 3rd Qu.:15.00 3rd Qu.:427.0
Max. :229.0 Max. :29.00 Max. :671.0
NA's :4864 NA's :99120 NA's :4915
id_20 id_21 id_22
Min. :100.0 Min. :100.0 Min. :10
1st Qu.:256.0 1st Qu.:252.0 1st Qu.:14
Median :472.0 Median :252.0 Median :14
Mean :403.9 Mean :368.3 Mean :16
3rd Qu.:533.0 3rd Qu.:486.5 3rd Qu.:14
Max. :661.0 Max. :854.0 Max. :44
NA's :4972 NA's :139074 NA's :139064
id_23 id_24 id_25
IP_PROXY:ANONYMOUS : 1071 Min. :11.0 Min. :100.0
IP_PROXY:HIDDEN : 609 1st Qu.:11.0 1st Qu.:321.0
IP_PROXY:TRANSPARENT: 3489 Median :11.0 Median :321.0
NA's :139064 Mean :12.8 Mean :329.6
3rd Qu.:15.0 3rd Qu.:371.0
Max. :26.0 Max. :548.0
NA's :139486 NA's :139101
id_26 id_27 id_28 id_29
Min. :100.0 Found : 5155 Found:76232 Found :74926
1st Qu.:119.0 NotFound: 14 New :64746 NotFound:66052
Median :149.0 NA's :139064 NA's : 3255 NA's : 3255
Mean :149.1
3rd Qu.:169.0
Max. :216.0
NA's :139070
id_30 id_31 id_32
Windows 10 :21155 chrome 63.0 :22000 Min. : 0.00
Windows 7 :13110 mobile safari 11.0 :13423 1st Qu.:24.00
iOS 11.2.1 : 3722 mobile safari generic:11474 Median :24.00
iOS 11.1.2 : 3699 ie 11.0 for desktop : 9030 Mean :26.51
Android 7.0: 2871 safari generic : 8195 3rd Qu.:32.00
(Other) :33008 (Other) :76160 Max. :32.00
NA's :66668 NA's : 3951 NA's :66647
id_33 id_34 id_35 id_36
1920x1080:16874 match_status:-1: 3 Mode :logical Mode :logical
1366x768 : 8605 match_status:0 : 415 FALSE:63171 FALSE:134066
1334x750 : 6447 match_status:1 :17376 TRUE :77814 TRUE :6919
2208x1242: 4900 match_status:2 :60011 NA's :3248 NA's :3248
1440x900 : 4384 NA's :66428
(Other) :32079
NA's :70944
id_37 id_38 DeviceType DeviceInfo
Mode :logical Mode :logical desktop:85165 Windows :47722
FALSE:30533 FALSE:73922 mobile :55645 iOS Device :19782
TRUE :110452 TRUE :67063 NA's : 3423 MacOS :12573
NA's :3248 NA's :3248 Trident/7.0: 7440
rv:11.0 : 1901
(Other) :29248
NA's :25567 test_i
str(test_i)'data.frame': 141907 obs. of 41 variables:
$ TransactionID: int 3663586 3663588 3663597 3663601 3663602 3663622 3663624 3663626 3663629 3663658 ...
$ id_01 : num -45 0 -5 -45 -95 -5 -5 -5 -5 -5 ...
$ id_02 : num 280290 3579 185210 252944 328680 ...
$ id_03 : num NA 0 NA 0 NA NA 0 0 0 NA ...
$ id_04 : num NA 0 NA 0 NA NA 0 0 0 NA ...
$ id_05 : num 0 0 1 0 7 4 2 0 2 0 ...
$ id_06 : num 0 0 0 0 -33 -2 -2 -2 -2 0 ...
$ id_07 : num NA NA NA NA NA NA NA NA NA NA ...
$ id_08 : num NA NA NA NA NA NA NA NA NA NA ...
$ id_09 : num NA 0 NA 0 NA NA 0 0 0 NA ...
$ id_10 : num NA 0 NA 0 NA NA 0 0 0 NA ...
$ id_11 : num 100 100 100 100 100 100 100 100 100 100 ...
$ id_12 : Factor w/ 2 levels "Found","NotFound": 2 1 2 2 2 2 1 1 1 1 ...
$ id_13 : num 27 NA 52 27 27 27 27 27 27 27 ...
$ id_14 : num NA -300 -360 NA NA -480 -480 -480 -480 -360 ...
$ id_15 : Factor w/ 3 levels "Found","New",..: 2 1 2 1 2 2 1 1 1 2 ...
$ id_16 : Factor w/ 2 levels "Found","NotFound": 2 1 2 1 2 2 1 1 1 2 ...
$ id_17 : num 225 166 225 225 225 166 166 166 166 166 ...
$ id_18 : num 15 NA NA 15 15 15 15 15 15 NA ...
$ id_19 : num 427 542 271 427 567 352 352 352 352 529 ...
$ id_20 : num 563 368 507 563 507 177 177 177 177 214 ...
$ id_21 : num NA NA NA NA NA NA NA NA NA NA ...
$ id_22 : num NA NA NA NA NA NA NA NA NA NA ...
$ id_23 : Factor w/ 3 levels "IP_PROXY:ANONYMOUS",..: NA NA NA NA NA NA NA NA NA NA ...
$ id_24 : num NA NA NA NA NA NA NA NA NA NA ...
$ id_25 : num NA NA NA NA NA NA NA NA NA NA ...
$ id_26 : num NA NA NA NA NA NA NA NA NA NA ...
$ id_27 : Factor w/ 2 levels "Found","NotFound": NA NA NA NA NA NA NA NA NA NA ...
$ id_28 : Factor w/ 2 levels "Found","New": 2 1 2 1 2 2 1 1 1 2 ...
$ id_29 : Factor w/ 2 levels "Found","NotFound": 2 1 2 1 2 2 1 1 1 2 ...
$ id_30 : Factor w/ 86 levels "Android","Android 4.4.2",..: NA 7 NA NA NA 37 37 37 37 37 ...
$ id_31 : Factor w/ 135 levels "android browser 4.0",..: 46 46 101 46 46 104 104 104 104 104 ...
$ id_32 : num NA 24 NA NA NA 32 32 32 32 32 ...
$ id_33 : Factor w/ 390 levels "1000x798","1021x669",..: NA 49 NA NA NA 219 219 219 219 197 ...
$ id_34 : Factor w/ 2 levels "match_status:1",..: NA 2 NA NA NA 2 2 2 2 2 ...
$ id_35 : logi FALSE TRUE FALSE FALSE FALSE TRUE ...
$ id_36 : logi FALSE FALSE TRUE FALSE FALSE FALSE ...
$ id_37 : logi TRUE TRUE TRUE TRUE TRUE FALSE ...
$ id_38 : logi FALSE TRUE FALSE FALSE FALSE TRUE ...
$ DeviceType : Factor w/ 2 levels "desktop","mobile": 2 2 1 2 2 2 2 2 2 2 ...
$ DeviceInfo : Factor w/ 2226 levels "2014819","2PS64",..: 1091 857 2069 1091 1768 598 598 598 598 598 ...summary(test_i) TransactionID id_01 id_02 id_03
Min. :3663586 Min. :-100.00 Min. : 2 Min. :-12.00
1st Qu.:3859268 1st Qu.: -12.50 1st Qu.: 63340 1st Qu.: 0.00
Median :4001774 Median : -5.00 Median :133190 Median : 0.00
Mean :3972166 Mean : -11.33 Mean :192659 Mean : 0.05
3rd Qu.:4105284 3rd Qu.: -5.00 3rd Qu.:265718 3rd Qu.: 0.00
Max. :4170239 Max. : 0.00 Max. :999869 Max. : 11.00
NA's :4931 NA's :75426
id_04 id_05 id_06 id_07
Min. :-19.00 Min. :-81.000 Min. :-100.000 Min. :-41.00
1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: -6.000 1st Qu.: 3.00
Median : 0.00 Median : 0.000 Median : 0.000 Median : 12.00
Mean : -0.09 Mean : 1.246 Mean : -6.804 Mean : 12.49
3rd Qu.: 0.00 3rd Qu.: 1.000 3rd Qu.: 0.000 3rd Qu.: 21.00
Max. : 0.00 Max. : 52.000 Max. : 0.000 Max. : 59.00
NA's :75426 NA's :7157 NA's :7157 NA's :136848
id_08 id_09 id_10 id_11
Min. :-100.00 Min. :-32.00 Min. :-100.00 Min. : 90.00
1st Qu.: -46.00 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.:100.00
Median : -33.00 Median : 0.00 Median : 0.00 Median :100.00
Mean : -36.58 Mean : 0.08 Mean : -0.25 Mean : 99.75
3rd Qu.: -23.00 3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.:100.00
Max. : 0.00 Max. : 16.00 Max. : 0.00 Max. :100.00
NA's :136848 NA's :67569 NA's :67569 NA's :5129
id_12 id_13 id_14 id_15
Found : 21012 Min. :11.00 Min. :-720.0 Found :67962
NotFound:120895 1st Qu.:27.00 1st Qu.:-360.0 New :57785
Median :27.00 Median :-300.0 Unknown:11230
Mean :36.91 Mean :-344.5 NA's : 4930
3rd Qu.:52.00 3rd Qu.:-300.0
Max. :63.00 Max. : 720.0
NA's :11621 NA's :70550
id_16 id_17 id_18 id_19
Found :66481 Min. :100.0 Min. :11.0 Min. :100.0
NotFound:59266 1st Qu.:166.0 1st Qu.:13.0 1st Qu.:266.0
NA's :16160 Median :166.0 Median :15.0 Median :321.0
Mean :191.1 Mean :14.8 Mean :350.1
3rd Qu.:225.0 3rd Qu.:15.0 3rd Qu.:427.0
Max. :228.0 Max. :29.0 Max. :670.0
NA's :5941 NA's :91032 NA's :6001
id_20 id_21 id_22
Min. :100.0 Min. :100.0 Min. :11.00
1st Qu.:256.0 1st Qu.:252.0 1st Qu.:14.00
Median :484.0 Median :576.0 Median :14.00
Mean :408.9 Mean :507.7 Mean :15.34
3rd Qu.:549.0 3rd Qu.:711.0 3rd Qu.:14.00
Max. :660.0 Max. :854.0 Max. :44.00
NA's :6274 NA's :136848 NA's :136845
id_23 id_24 id_25
IP_PROXY:ANONYMOUS : 939 Min. :10.00 Min. :100
IP_PROXY:HIDDEN : 409 1st Qu.:11.00 1st Qu.:321
IP_PROXY:TRANSPARENT: 3714 Median :11.00 Median :321
NA's :136845 Mean :13.17 Mean :332
3rd Qu.:15.00 3rd Qu.:355
Max. :26.00 Max. :549
NA's :137167 NA's :136868
id_26 id_27 id_28 id_29
Min. :100.0 Found : 5059 Found:75581 Found :74338
1st Qu.:137.0 NotFound: 3 New :61197 NotFound:62440
Median :147.0 NA's :136845 NA's : 5129 NA's : 5129
Mean :152.8
3rd Qu.:182.0
Max. :216.0
NA's :136860
id_30 id_31 id_32
Windows 10 :21015 chrome 70.0 :16054 Min. : 8.00
Windows 7 :10368 mobile safari 12.0:13098 1st Qu.:24.00
iOS 12.1.0 : 6349 mobile safari 11.0:10232 Median :24.00
iOS 11.4.1 : 3538 chrome 71.0 : 9489 Mean :26.22
Mac OS X 10_13_6: 3254 chrome 69.0 : 8293 3rd Qu.:32.00
(Other) :26135 (Other) :79459 Max. :48.00
NA's :71248 NA's : 5282 NA's :71236
id_33 id_34 id_35 id_36
1920x1080:16868 match_status:1: 1 Mode :logical Mode :logical
1366x768 : 6441 match_status:2:72174 FALSE:65327 FALSE:133287
1334x750 : 5103 NA's :69732 TRUE :71650 TRUE :3690
2208x1242: 4213 NA's :4930 NA's :4930
1440x900 : 3743
(Other) :34303
NA's :71236
id_37 id_38 DeviceType DeviceInfo
Mode :logical Mode :logical desktop:74403 Windows :44988
FALSE:32280 FALSE:95058 mobile :62528 iOS Device :18720
TRUE :104697 TRUE :41919 NA's : 4976 MacOS :11149
NA's :4930 NA's :4930 Trident/7.0: 4890
rv:11.0 : 749
(Other) :34561
NA's :26850 train_t
str(train_t)'data.frame': 590540 obs. of 394 variables:
$ TransactionID : int 2987000 2987001 2987002 2987003 2987004 2987005 2987006 2987007 2987008 2987009 ...
$ isFraud : int 0 0 0 0 0 0 0 0 0 0 ...
$ TransactionDT : int 86400 86401 86469 86499 86506 86510 86522 86529 86535 86536 ...
$ TransactionAmt: num 68.5 29 59 50 50 ...
$ ProductCD : Factor w/ 5 levels "C","H","R","S",..: 5 5 5 5 2 5 5 5 2 5 ...
$ card1 : int 13926 2755 4663 18132 4497 5937 12308 12695 2803 17399 ...
$ card2 : num NA 404 490 567 514 555 360 490 100 111 ...
$ card3 : num 150 150 150 150 150 150 150 150 150 150 ...
$ card4 : Factor w/ 4 levels "american express",..: 2 3 4 3 3 4 4 4 4 3 ...
$ card5 : num 142 102 166 117 102 226 166 226 226 224 ...
$ card6 : Factor w/ 4 levels "charge card",..: 2 2 3 3 2 3 3 3 3 3 ...
$ addr1 : num 315 325 330 476 420 272 126 325 337 204 ...
$ addr2 : num 87 87 87 87 87 87 87 87 87 87 ...
$ dist1 : num 19 NA 287 NA NA 36 0 NA NA 19 ...
$ dist2 : num NA NA NA NA NA NA NA NA NA NA ...
$ P_emaildomain : Factor w/ 59 levels "aim.com","anonymous.com",..: NA 17 36 54 17 17 54 30 2 54 ...
$ R_emaildomain : Factor w/ 60 levels "aim.com","anonymous.com",..: NA NA NA NA NA NA NA NA NA NA ...
$ C1 : num 1 1 1 2 1 1 1 1 1 2 ...
$ C2 : num 1 1 1 5 1 1 1 1 1 2 ...
$ C3 : num 0 0 0 0 0 0 0 0 0 0 ...
$ C4 : num 0 0 0 0 0 0 0 0 0 0 ...
$ C5 : num 0 0 0 0 0 0 0 0 0 0 ...
$ C6 : num 1 1 1 4 1 1 1 1 1 3 ...
$ C7 : num 0 0 0 0 0 0 0 0 0 0 ...
$ C8 : num 0 0 0 0 1 0 0 0 1 0 ...
$ C9 : num 1 0 1 1 0 1 1 0 0 3 ...
$ C10 : num 0 0 0 0 1 0 0 0 1 0 ...
$ C11 : num 2 1 1 1 1 1 1 1 1 1 ...
$ C12 : num 0 0 0 0 0 0 0 0 0 0 ...
$ C13 : num 1 1 1 25 1 1 1 1 1 12 ...
$ C14 : num 1 1 1 1 1 1 1 1 1 2 ...
$ D1 : num 14 0 0 112 0 0 0 0 0 61 ...
$ D2 : num NA NA NA 112 NA NA NA NA NA 61 ...
$ D3 : num 13 NA NA 0 NA NA NA NA NA 30 ...
$ D4 : num NA 0 0 94 NA 0 0 0 NA 318 ...
$ D5 : num NA NA NA 0 NA NA NA NA NA 30 ...
$ D6 : num NA NA NA NA NA NA NA NA NA NA ...
$ D7 : num NA NA NA NA NA NA NA NA NA NA ...
$ D8 : num NA NA NA NA NA NA NA NA NA NA ...
$ D9 : num NA NA NA NA NA NA NA NA NA NA ...
$ D10 : num 13 0 0 84 NA 0 0 0 NA 40 ...
$ D11 : num 13 NA 315 NA NA 0 0 NA NA 302 ...
$ D12 : num NA NA NA NA NA NA NA NA NA NA ...
$ D13 : num NA NA NA NA NA NA NA NA NA NA ...
$ D14 : num NA NA NA NA NA NA NA NA NA NA ...
$ D15 : num 0 0 315 111 NA 0 0 0 NA 318 ...
$ M1 : logi TRUE NA TRUE NA NA TRUE ...
$ M2 : logi TRUE NA TRUE NA NA TRUE ...
$ M3 : logi TRUE NA TRUE NA NA TRUE ...
$ M4 : Factor w/ 3 levels "M0","M1","M2": 3 1 1 1 NA 2 1 1 NA 1 ...
$ M5 : logi FALSE TRUE FALSE TRUE NA FALSE ...
$ M6 : logi TRUE TRUE FALSE FALSE NA TRUE ...
$ M7 : logi NA NA FALSE NA NA NA ...
$ M8 : logi NA NA FALSE NA NA NA ...
$ M9 : logi NA NA FALSE NA NA NA ...
$ V1 : num 1 NA 1 NA NA 1 1 NA NA 1 ...
$ V2 : num 1 NA 1 NA NA 1 1 NA NA 1 ...
$ V3 : num 1 NA 1 NA NA 1 1 NA NA 1 ...
$ V4 : num 1 NA 1 NA NA 1 1 NA NA 1 ...
$ V5 : num 1 NA 1 NA NA 1 1 NA NA 1 ...
$ V6 : num 1 NA 1 NA NA 1 1 NA NA 1 ...
$ V7 : num 1 NA 1 NA NA 1 1 NA NA 1 ...
$ V8 : num 1 NA 1 NA NA 1 1 NA NA 1 ...
$ V9 : num 1 NA 1 NA NA 1 1 NA NA 1 ...
$ V10 : num 0 NA 0 NA NA 0 0 NA NA 0 ...
$ V11 : num 0 NA 0 NA NA 0 0 NA NA 0 ...
$ V12 : num 1 0 1 1 NA 1 1 0 NA 1 ...
$ V13 : num 1 0 1 1 NA 1 1 0 NA 1 ...
$ V14 : num 1 1 1 1 NA 1 1 1 NA 1 ...
$ V15 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V16 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V17 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V18 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V19 : num 1 1 1 1 NA 1 1 1 NA 1 ...
$ V20 : num 1 1 1 1 NA 1 1 1 NA 1 ...
$ V21 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V22 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V23 : num 1 1 1 1 NA 1 1 1 NA 1 ...
$ V24 : num 1 1 1 1 NA 1 1 1 NA 1 ...
$ V25 : num 1 1 1 1 NA 1 1 1 NA 1 ...
$ V26 : num 1 1 1 1 NA 1 1 1 NA 1 ...
$ V27 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V28 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V29 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V30 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V31 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V32 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V33 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V34 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V35 : num NA 0 1 1 NA 1 1 0 NA 1 ...
$ V36 : num NA 0 1 1 NA 1 1 0 NA 1 ...
$ V37 : num NA 1 1 1 NA 1 1 1 NA 1 ...
$ V38 : num NA 1 1 1 NA 1 1 1 NA 1 ...
$ V39 : num NA 0 0 0 NA 0 0 0 NA 0 ...
$ V40 : num NA 0 0 0 NA 0 0 0 NA 0 ...
$ V41 : num NA 1 1 1 NA 1 1 1 NA 1 ...
$ V42 : num NA 0 0 0 NA 0 0 0 NA 0 ...
$ V43 : num NA 0 0 0 NA 0 0 0 NA 0 ...
$ V44 : num NA 1 1 1 NA 1 1 1 NA 1 ...
[list output truncated]summary(train_t) TransactionID isFraud TransactionDT TransactionAmt
Min. :2987000 Min. :0.00000 Min. : 86400 Min. : 0.25
1st Qu.:3134635 1st Qu.:0.00000 1st Qu.: 3027058 1st Qu.: 43.32
Median :3282270 Median :0.00000 Median : 7306528 Median : 68.77
Mean :3282270 Mean :0.03499 Mean : 7372311 Mean : 135.03
3rd Qu.:3429904 3rd Qu.:0.00000 3rd Qu.:11246620 3rd Qu.: 125.00
Max. :3577539 Max. :1.00000 Max. :15811131 Max. :31937.39
ProductCD card1 card2 card3
C: 68519 Min. : 1000 Min. :100.0 Min. :100.0
H: 33024 1st Qu.: 6019 1st Qu.:214.0 1st Qu.:150.0
R: 37699 Median : 9678 Median :361.0 Median :150.0
S: 11628 Mean : 9899 Mean :362.6 Mean :153.2
W:439670 3rd Qu.:14184 3rd Qu.:512.0 3rd Qu.:150.0
Max. :18396 Max. :600.0 Max. :231.0
NA's :8933 NA's :1565
card4 card5 card6
american express: 8328 Min. :100.0 charge card : 15
discover : 6651 1st Qu.:166.0 credit :148986
mastercard :189217 Median :226.0 debit :439938
visa :384767 Mean :199.3 debit or credit: 30
NA's : 1577 3rd Qu.:226.0 NA's : 1571
Max. :237.0
NA's :4259
addr1 addr2 dist1 dist2
Min. :100.0 Min. : 10.0 Min. : 0.0 Min. : 0.0
1st Qu.:204.0 1st Qu.: 87.0 1st Qu.: 3.0 1st Qu.: 7.0
Median :299.0 Median : 87.0 Median : 8.0 Median : 37.0
Mean :290.7 Mean : 86.8 Mean : 118.5 Mean : 231.9
3rd Qu.:330.0 3rd Qu.: 87.0 3rd Qu.: 24.0 3rd Qu.: 206.0
Max. :540.0 Max. :102.0 Max. :10286.0 Max. :11623.0
NA's :65706 NA's :65706 NA's :352271 NA's :552913
P_emaildomain R_emaildomain C1
gmail.com :228355 gmail.com : 57147 Min. : 0.00
yahoo.com :100934 hotmail.com : 27509 1st Qu.: 1.00
hotmail.com : 45250 anonymous.com: 20529 Median : 1.00
anonymous.com: 36998 yahoo.com : 11842 Mean : 14.09
aol.com : 28289 aol.com : 3701 3rd Qu.: 3.00
(Other) : 56258 (Other) : 16563 Max. :4685.00
NA's : 94456 NA's :453249
C2 C3 C4
Min. : 0.00 Min. : 0.000000 Min. : 0.000
1st Qu.: 1.00 1st Qu.: 0.000000 1st Qu.: 0.000
Median : 1.00 Median : 0.000000 Median : 0.000
Mean : 15.27 Mean : 0.005644 Mean : 4.092
3rd Qu.: 3.00 3rd Qu.: 0.000000 3rd Qu.: 0.000
Max. :5691.00 Max. :26.000000 Max. :2253.000
C5 C6 C7
Min. : 0.000 Min. : 0.000 Min. : 0.000
1st Qu.: 0.000 1st Qu.: 1.000 1st Qu.: 0.000
Median : 0.000 Median : 1.000 Median : 0.000
Mean : 5.572 Mean : 9.071 Mean : 2.849
3rd Qu.: 1.000 3rd Qu.: 2.000 3rd Qu.: 0.000
Max. :349.000 Max. :2253.000 Max. :2255.000
C8 C9 C10 C11
Min. : 0.000 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 1.00
Median : 0.000 Median : 1.00 Median : 0.00 Median : 1.00
Mean : 5.145 Mean : 4.48 Mean : 5.24 Mean : 10.24
3rd Qu.: 0.000 3rd Qu.: 2.00 3rd Qu.: 0.00 3rd Qu.: 2.00
Max. :3331.000 Max. :210.00 Max. :3257.00 Max. :3188.00
C12 C13 C14 D1
Min. : 0.000 Min. : 0.00 Min. : 0.000 Min. : 0.00
1st Qu.: 0.000 1st Qu.: 1.00 1st Qu.: 1.000 1st Qu.: 0.00
Median : 0.000 Median : 3.00 Median : 1.000 Median : 3.00
Mean : 4.076 Mean : 32.54 Mean : 8.295 Mean : 94.35
3rd Qu.: 0.000 3rd Qu.: 12.00 3rd Qu.: 2.000 3rd Qu.:122.00
Max. :3188.000 Max. :2918.00 Max. :1429.000 Max. :640.00
NA's :1269
D2 D3 D4 D5
Min. : 0.0 Min. : 0.00 Min. :-122 Min. : 0.00
1st Qu.: 26.0 1st Qu.: 1.00 1st Qu.: 0 1st Qu.: 1.00
Median : 97.0 Median : 8.00 Median : 26 Median : 10.00
Mean :169.6 Mean : 28.34 Mean : 140 Mean : 42.34
3rd Qu.:276.0 3rd Qu.: 27.00 3rd Qu.: 253 3rd Qu.: 32.00
Max. :640.0 Max. :819.00 Max. : 869 Max. :819.00
NA's :280797 NA's :262878 NA's :168922 NA's :309841
D6 D7 D8 D9
Min. :-83.0 Min. : 0.0 Min. : 0.0 Min. :0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 1.0 1st Qu.:0.2
Median : 0.0 Median : 0.0 Median : 37.9 Median :0.7
Mean : 69.8 Mean : 41.6 Mean : 146.1 Mean :0.6
3rd Qu.: 40.0 3rd Qu.: 17.0 3rd Qu.: 188.0 3rd Qu.:0.8
Max. :873.0 Max. :843.0 Max. :1707.8 Max. :1.0
NA's :517353 NA's :551623 NA's :515614 NA's :515614
D10 D11 D12 D13
Min. : 0 Min. :-53.0 Min. :-83 Min. : 0.0
1st Qu.: 0 1st Qu.: 0.0 1st Qu.: 0 1st Qu.: 0.0
Median : 15 Median : 43.0 Median : 0 Median : 0.0
Mean :124 Mean :146.6 Mean : 54 Mean : 17.9
3rd Qu.:197 3rd Qu.:274.0 3rd Qu.: 13 3rd Qu.: 0.0
Max. :876 Max. :670.0 Max. :648 Max. :847.0
NA's :76022 NA's :279287 NA's :525823 NA's :528588
D14 D15 M1 M2
Min. :-193.0 Min. :-83.0 Mode :logical Mode :logical
1st Qu.: 0.0 1st Qu.: 0.0 FALSE:25 FALSE:33972
Median : 0.0 Median : 52.0 TRUE :319415 TRUE :285468
Mean : 57.7 Mean :163.7 NA's :271100 NA's :271100
3rd Qu.: 2.0 3rd Qu.:314.0
Max. : 878.0 Max. :879.0
NA's :528353 NA's :89113
M3 M4 M5 M6
Mode :logical M0 :196405 Mode :logical Mode :logical
FALSE:67709 M1 : 52826 FALSE:132491 FALSE:227856
TRUE :251731 M2 : 59865 TRUE :107567 TRUE :193324
NA's :271100 NA's:281444 NA's :350482 NA's :169360
M7 M8 M9 V1
Mode :logical Mode :logical Mode :logical Min. :0
FALSE:211374 FALSE:155251 FALSE:38632 1st Qu.:1
TRUE :32901 TRUE :89037 TRUE :205656 Median :1
NA's :346265 NA's :346252 NA's :346252 Mean :1
3rd Qu.:1
Max. :1
NA's :279287
V2 V3 V4 V5
Min. :0.00 Min. :0.00 Min. :0.00 Min. :0.00
1st Qu.:1.00 1st Qu.:1.00 1st Qu.:1.00 1st Qu.:1.00
Median :1.00 Median :1.00 Median :1.00 Median :1.00
Mean :1.05 Mean :1.08 Mean :0.85 Mean :0.88
3rd Qu.:1.00 3rd Qu.:1.00 3rd Qu.:1.00 3rd Qu.:1.00
Max. :8.00 Max. :9.00 Max. :6.00 Max. :6.00
NA's :279287 NA's :279287 NA's :279287 NA's :279287
V6 V7 V8 V9
Min. :0.00 Min. :0.00 Min. :0.00 Min. :0.00
1st Qu.:1.00 1st Qu.:1.00 1st Qu.:1.00 1st Qu.:1.00
Median :1.00 Median :1.00 Median :1.00 Median :1.00
Mean :1.05 Mean :1.07 Mean :1.03 Mean :1.04
3rd Qu.:1.00 3rd Qu.:1.00 3rd Qu.:1.00 3rd Qu.:1.00
Max. :9.00 Max. :9.00 Max. :8.00 Max. :8.00
NA's :279287 NA's :279287 NA's :279287 NA's :279287
V10 V11 V12 V13
Min. :0.00 Min. :0.00 Min. :0.00 Min. :0.0
1st Qu.:0.00 1st Qu.:0.00 1st Qu.:0.00 1st Qu.:0.0
Median :0.00 Median :0.00 Median :1.00 Median :1.0
Mean :0.46 Mean :0.48 Mean :0.56 Mean :0.6
3rd Qu.:1.00 3rd Qu.:1.00 3rd Qu.:1.00 3rd Qu.:1.0
Max. :4.00 Max. :5.00 Max. :3.00 Max. :6.0
NA's :279287 NA's :279287 NA's :76073 NA's :76073
V14 V15 V16 V17
Min. :0 Min. :0.00 Min. : 0.00 Min. : 0.00
1st Qu.:1 1st Qu.:0.00 1st Qu.: 0.00 1st Qu.: 0.00
Median :1 Median :0.00 Median : 0.00 Median : 0.00
Mean :1 Mean :0.12 Mean : 0.12 Mean : 0.13
3rd Qu.:1 3rd Qu.:0.00 3rd Qu.: 0.00 3rd Qu.: 0.00
Max. :1 Max. :7.00 Max. :15.00 Max. :15.00
NA's :76073 NA's :76073 NA's :76073 NA's :76073
V18 V19 V20 V21
Min. : 0.00 Min. :0.00 Min. : 0.00 Min. :0.00
1st Qu.: 0.00 1st Qu.:1.00 1st Qu.: 1.00 1st Qu.:0.00
Median : 0.00 Median :1.00 Median : 1.00 Median :0.00
Mean : 0.14 Mean :0.82 Mean : 0.85 Mean :0.13
3rd Qu.: 0.00 3rd Qu.:1.00 3rd Qu.: 1.00 3rd Qu.:0.00
Max. :15.00 Max. :7.00 Max. :15.00 Max. :5.00
NA's :76073 NA's :76073 NA's :76073 NA's :76073
V22 V23 V24 V25
Min. :0.00 Min. : 0.00 Min. : 0.00 Min. :0.00
1st Qu.:0.00 1st Qu.: 1.00 1st Qu.: 1.00 1st Qu.:1.00
Median :0.00 Median : 1.00 Median : 1.00 Median :1.00
Mean :0.13 Mean : 1.03 Mean : 1.06 Mean :0.98
3rd Qu.:0.00 3rd Qu.: 1.00 3rd Qu.: 1.00 3rd Qu.:1.00
Max. :8.00 Max. :13.00 Max. :13.00 Max. :7.00
NA's :76073 NA's :76073 NA's :76073 NA's :76073
V26 V27 V28 V29
Min. : 0.00 Min. :0 Min. :0 Min. :0.00
1st Qu.: 1.00 1st Qu.:0 1st Qu.:0 1st Qu.:0.00
Median : 1.00 Median :0 Median :0 Median :0.00
Mean : 0.99 Mean :0 Mean :0 Mean :0.39
3rd Qu.: 1.00 3rd Qu.:0 3rd Qu.:0 3rd Qu.:1.00
Max. :13.00 Max. :4 Max. :4 Max. :5.00
NA's :76073 NA's :76073 NA's :76073 NA's :76073
V30 V31 V32 V33
Min. :0.00 Min. :0.00 Min. : 0.00 Min. :0.00
1st Qu.:0.00 1st Qu.:0.00 1st Qu.: 0.00 1st Qu.:0.00
Median :0.00 Median :0.00 Median : 0.00 Median :0.00
Mean :0.41 Mean :0.14 Mean : 0.14 Mean :0.13
3rd Qu.:1.00 3rd Qu.:0.00 3rd Qu.: 0.00 3rd Qu.:0.00
Max. :9.00 Max. :7.00 Max. :15.00 Max. :7.00
NA's :76073 NA's :76073 NA's :76073 NA's :76073
V34 V35 V36 V37
Min. : 0.00 Min. :0.00 Min. :0.00 Min. : 0.00
1st Qu.: 0.00 1st Qu.:0.00 1st Qu.:0.00 1st Qu.: 1.00
Median : 0.00 Median :1.00 Median :1.00 Median : 1.00
Mean : 0.14 Mean :0.54 Mean :0.58 Mean : 1.11
3rd Qu.: 0.00 3rd Qu.:1.00 3rd Qu.:1.00 3rd Qu.: 1.00
Max. :13.00 Max. :3.00 Max. :5.00 Max. :54.00
NA's :76073 NA's :168969 NA's :168969 NA's :168969
V38 V39 V40 V41
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. :0
1st Qu.: 1.00 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.:1
Median : 1.00 Median : 0.00 Median : 0.00 Median :1
Mean : 1.16 Mean : 0.17 Mean : 0.18 Mean :1
3rd Qu.: 1.00 3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.:1
Max. :54.00 Max. :15.00 Max. :24.00 Max. :1
NA's :168969 NA's :168969 NA's :168969 NA's :168969
V42 V43 V44 V45
Min. :0.00 Min. :0.00 Min. : 0.00 Min. : 0.00
1st Qu.:0.00 1st Qu.:0.00 1st Qu.: 1.00 1st Qu.: 1.00
Median :0.00 Median :0.00 Median : 1.00 Median : 1.00
Mean :0.16 Mean :0.17 Mean : 1.08 Mean : 1.12
3rd Qu.:0.00 3rd Qu.:0.00 3rd Qu.: 1.00 3rd Qu.: 1.00
Max. :8.00 Max. :8.00 Max. :48.00 Max. :48.00
NA's :168969 NA's :168969 NA's :168969 NA's :168969
V46 V47 V48 V49
Min. :0.00 Min. : 0.00 Min. :0.00 Min. :0.0
1st Qu.:1.00 1st Qu.: 1.00 1st Qu.:0.00 1st Qu.:0.0
Median :1.00 Median : 1.00 Median :0.00 Median :0.0
Mean :1.02 Mean : 1.04 Mean :0.38 Mean :0.4
3rd Qu.:1.00 3rd Qu.: 1.00 3rd Qu.:1.00 3rd Qu.:1.0
Max. :6.00 Max. :12.00 Max. :5.00 Max. :5.0
NA's :168969 NA's :168969 NA's :168969 NA's :168969
V50 V51 V52 V53
Min. :0.00 Min. :0.00 Min. : 0.00 Min. :0.00
1st Qu.:0.00 1st Qu.:0.00 1st Qu.: 0.00 1st Qu.:0.00
Median :0.00 Median :0.00 Median : 0.00 Median :1.00
Mean :0.16 Mean :0.17 Mean : 0.18 Mean :0.58
3rd Qu.:0.00 3rd Qu.:0.00 3rd Qu.: 0.00 3rd Qu.:1.00
Max. :5.00 Max. :6.00 Max. :12.00 Max. :5.00
NA's :168969 NA's :168969 NA's :168969 NA's :77096
V54 V55 V56 V57
Min. :0.00 Min. : 0.00 Min. : 0.00 Min. :0.00
1st Qu.:0.00 1st Qu.: 1.00 1st Qu.: 1.00 1st Qu.:0.00
Median :1.00 Median : 1.00 Median : 1.00 Median :0.00
Mean :0.62 Mean : 1.07 Mean : 1.12 Mean :0.13
3rd Qu.:1.00 3rd Qu.: 1.00 3rd Qu.: 1.00 3rd Qu.:0.00
Max. :6.00 Max. :17.00 Max. :51.00 Max. :6.00
NA's :77096 NA's :77096 NA's :77096 NA's :77096
V58 V59 V60 V61
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. :0.00
1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.:1.00
Median : 0.00 Median : 0.00 Median : 0.00 Median :1.00
Mean : 0.13 Mean : 0.13 Mean : 0.14 Mean :0.83
3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.:1.00
Max. :10.00 Max. :16.00 Max. :16.00 Max. :6.00
NA's :77096 NA's :77096 NA's :77096 NA's :77096
V62 V63 V64 V65
Min. : 0.00 Min. :0.00 Min. :0.00 Min. :0
1st Qu.: 1.00 1st Qu.:0.00 1st Qu.:0.00 1st Qu.:1
Median : 1.00 Median :0.00 Median :0.00 Median :1
Mean : 0.87 Mean :0.13 Mean :0.14 Mean :1
3rd Qu.: 1.00 3rd Qu.:0.00 3rd Qu.:0.00 3rd Qu.:1
Max. :10.00 Max. :7.00 Max. :7.00 Max. :1
NA's :77096 NA's :77096 NA's :77096 NA's :77096
V66 V67 V68 V69
Min. :0.00 Min. :0 Min. :0 Min. :0.00
1st Qu.:1.00 1st Qu.:1 1st Qu.:0 1st Qu.:0.00
Median :1.00 Median :1 Median :0 Median :0.00
Mean :0.98 Mean :1 Mean :0 Mean :0.39
3rd Qu.:1.00 3rd Qu.:1 3rd Qu.:0 3rd Qu.:1.00
Max. :7.00 Max. :8 Max. :2 Max. :5.00
NA's :77096 NA's :77096 NA's :77096 NA's :77096
V70 V71 V72 V73
Min. :0.00 Min. :0.00 Min. : 0.00 Min. :0.00
1st Qu.:0.00 1st Qu.:0.00 1st Qu.: 0.00 1st Qu.:0.00
Median :0.00 Median :0.00 Median : 0.00 Median :0.00
Mean :0.41 Mean :0.14 Mean : 0.15 Mean :0.14
3rd Qu.:1.00 3rd Qu.:0.00 3rd Qu.: 0.00 3rd Qu.:0.00
Max. :6.00 Max. :6.00 Max. :10.00 Max. :7.00
NA's :77096 NA's :77096 NA's :77096 NA's :77096
V74 V75 V76 V77
Min. :0.00 Min. :0.00 Min. :0.00 Min. : 0.00
1st Qu.:0.00 1st Qu.:0.00 1st Qu.:0.00 1st Qu.: 1.00
Median :0.00 Median :1.00 Median :1.00 Median : 1.00
Mean :0.15 Mean :0.54 Mean :0.59 Mean : 1.09
3rd Qu.:0.00 3rd Qu.:1.00 3rd Qu.:1.00 3rd Qu.: 1.00
Max. :8.00 Max. :4.00 Max. :6.00 Max. :30.00
NA's :77096 NA's :89164 NA's :89164 NA's :89164
V78 V79 V80 V81
Min. : 0.00 Min. :0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 1.00 1st Qu.:0.00 1st Qu.: 0.00 1st Qu.: 0.00
Median : 1.00 Median :0.00 Median : 0.00 Median : 0.00
Mean : 1.14 Mean :0.14 Mean : 0.14 Mean : 0.15
3rd Qu.: 1.00 3rd Qu.:0.00 3rd Qu.: 0.00 3rd Qu.: 0.00
Max. :31.00 Max. :7.00 Max. :19.00 Max. :19.00
NA's :89164 NA's :89164 NA's :89164 NA's :89164
V82 V83 V84 V85
Min. :0.00 Min. :0.00 Min. :0.00 Min. :0.00
1st Qu.:1.00 1st Qu.:1.00 1st Qu.:0.00 1st Qu.:0.00
Median :1.00 Median :1.00 Median :0.00 Median :0.00
Mean :0.84 Mean :0.88 Mean :0.14 Mean :0.15
3rd Qu.:1.00 3rd Qu.:1.00 3rd Qu.:0.00 3rd Qu.:0.00
Max. :7.00 Max. :7.00 Max. :7.00 Max. :7.00
NA's :89164 NA's :89164 NA's :89164 NA's :89164
V86 V87 V88 V89
Min. : 0.00 Min. : 0.0 Min. :0 Min. :0
1st Qu.: 1.00 1st Qu.: 1.0 1st Qu.:1 1st Qu.:0
Median : 1.00 Median : 1.0 Median :1 Median :0
Mean : 1.06 Mean : 1.1 Mean :1 Mean :0
3rd Qu.: 1.00 3rd Qu.: 1.0 3rd Qu.:1 3rd Qu.:0
Max. :30.00 Max. :30.0 Max. :1 Max. :2
NA's :89164 NA's :89164 NA's :89164 NA's :89164
V90 V91 V92 V93
Min. :0.0 Min. :0.00 Min. :0.00 Min. :0.00
1st Qu.:0.0 1st Qu.:0.00 1st Qu.:0.00 1st Qu.:0.00
Median :0.0 Median :0.00 Median :0.00 Median :0.00
Mean :0.4 Mean :0.42 Mean :0.15 Mean :0.15
3rd Qu.:1.0 3rd Qu.:1.00 3rd Qu.:0.00 3rd Qu.:0.00
Max. :5.0 Max. :6.00 Max. :7.00 Max. :7.00
NA's :89164 NA's :89164 NA's :89164 NA's :89164
V94 V95 V96 V97
Min. :0.00 Min. : 0.000 Min. : 0.000 Min. : 0.000
1st Qu.:0.00 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
Median :0.00 Median : 0.000 Median : 0.000 Median : 0.000
Mean :0.14 Mean : 1.038 Mean : 3.005 Mean : 1.719
3rd Qu.:0.00 3rd Qu.: 0.000 3rd Qu.: 1.000 3rd Qu.: 0.000
Max. :2.00 Max. :880.000 Max. :1410.000 Max. :976.000
NA's :89164 NA's :314 NA's :314 NA's :314
V98 V99 V100 V101
Min. : 0.00000 Min. : 0.000 Min. : 0.0000 Min. : 0.0000
1st Qu.: 0.00000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.0000
Median : 0.00000 Median : 0.000 Median : 0.0000 Median : 0.0000
Mean : 0.06198 Mean : 0.895 Mean : 0.2735 Mean : 0.8892
3rd Qu.: 0.00000 3rd Qu.: 1.000 3rd Qu.: 0.0000 3rd Qu.: 0.0000
Max. :12.00000 Max. :88.000 Max. :28.0000 Max. :869.0000
NA's :314 NA's :314 NA's :314 NA's :314
V102 V103 V104 V105
Min. : 0.000 Min. : 0.000 Min. : 0.00000 Min. : 0.0000
1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.00000 1st Qu.: 0.0000
Median : 0.000 Median : 0.000 Median : 0.00000 Median : 0.0000
Mean : 1.827 Mean : 1.279 Mean : 0.08543 Mean : 0.2811
3rd Qu.: 0.000 3rd Qu.: 0.000 3rd Qu.: 0.00000 3rd Qu.: 0.0000
Max. :1285.000 Max. :928.000 Max. :15.00000 Max. :99.0000
NA's :314 NA's :314 NA's :314 NA's :314
V106 V107 V108 V109
Min. : 0.0000 Min. :0.0000 Min. :0.000 Min. :0.000
1st Qu.: 0.0000 1st Qu.:1.0000 1st Qu.:1.000 1st Qu.:1.000
Median : 0.0000 Median :1.0000 Median :1.000 Median :1.000
Mean : 0.1646 Mean :0.9996 Mean :1.005 Mean :1.015
3rd Qu.: 0.0000 3rd Qu.:1.0000 3rd Qu.:1.000 3rd Qu.:1.000
Max. :55.0000 Max. :1.0000 Max. :7.000 Max. :7.000
NA's :314 NA's :314 NA's :314 NA's :314
V110 V111 V112 V113
Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
Median :1.000 Median :1.000 Median :1.000 Median :1.000
Mean :1.008 Mean :1.003 Mean :1.005 Mean :1.003
3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
Max. :7.000 Max. :9.000 Max. :9.000 Max. :9.000
NA's :314 NA's :314 NA's :314 NA's :314
V114 V115 V116 V117
Min. :0.000 Min. :0.000 Min. :0.000 Min. :0
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1
Median :1.000 Median :1.000 Median :1.000 Median :1
Mean :1.009 Mean :1.032 Mean :1.016 Mean :1
3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1
Max. :6.000 Max. :6.000 Max. :6.000 Max. :3
NA's :314 NA's :314 NA's :314 NA's :314
V118 V119 V120 V121
Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
Median :1.000 Median :1.000 Median :1.000 Median :1.000
Mean :1.001 Mean :1.001 Mean :1.001 Mean :1.004
3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
Max. :3.000 Max. :3.000 Max. :3.000 Max. :3.000
NA's :314 NA's :314 NA's :314 NA's :314
V122 V123 V124 V125
Min. :0.000 Min. : 0.000 Min. : 0.000 Min. : 0.00
1st Qu.:1.000 1st Qu.: 1.000 1st Qu.: 1.000 1st Qu.: 1.00
Median :1.000 Median : 1.000 Median : 1.000 Median : 1.00
Mean :1.002 Mean : 1.031 Mean : 1.093 Mean : 1.05
3rd Qu.:1.000 3rd Qu.: 1.000 3rd Qu.: 1.000 3rd Qu.: 1.00
Max. :3.000 Max. :13.000 Max. :13.000 Max. :13.00
NA's :314 NA's :314 NA's :314 NA's :314
V126 V127 V128 V129
Min. : 0 Min. : 0.0 Min. : 0.0 Min. : 0.00
1st Qu.: 0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.00
Median : 0 Median : 0.0 Median : 0.0 Median : 0.00
Mean : 130 Mean : 336.6 Mean : 204.1 Mean : 8.77
3rd Qu.: 0 3rd Qu.: 108.0 3rd Qu.: 0.0 3rd Qu.: 0.00
Max. :160000 Max. :160000.0 Max. :160000.0 Max. :55125.00
NA's :314 NA's :314 NA's :314 NA's :314
V130 V131 V132
Min. : 0.00 Min. : 0.00 Min. : 0.0
1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.0
Median : 0.00 Median : 0.00 Median : 0.0
Mean : 92.17 Mean : 31.13 Mean : 103.5
3rd Qu.: 59.00 3rd Qu.: 0.00 3rd Qu.: 0.0
Max. :55125.00 Max. :55125.00 Max. :93736.0
NA's :314 NA's :314 NA's :314
V133 V134 V135 V136
Min. : 0.0 Min. : 0 Min. : 0.00 Min. : 0.00
1st Qu.: 0.0 1st Qu.: 0 1st Qu.: 0.00 1st Qu.: 0.00
Median : 0.0 Median : 0 Median : 0.00 Median : 0.00
Mean : 204.9 Mean : 146 Mean : 17.25 Mean : 38.82
3rd Qu.: 0.0 3rd Qu.: 0 3rd Qu.: 0.00 3rd Qu.: 0.00
Max. :133915.0 Max. :98476 Max. :90750.00 Max. :90750.00
NA's :314 NA's :314 NA's :314 NA's :314
V137 V138 V139 V140
Min. : 0.00 Min. : 0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.00 1st Qu.: 0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.00 Median : 0 Median : 1.0 Median : 1.0
Mean : 26.37 Mean : 0 Mean : 1.1 Mean : 1.1
3rd Qu.: 0.00 3rd Qu.: 0 3rd Qu.: 1.0 3rd Qu.: 1.0
Max. :90750.00 Max. :22 Max. :33.0 Max. :33.0
NA's :314 NA's :508595 NA's :508595 NA's :508595
V141 V142 V143 V144
Min. :0 Min. :0 Min. : 0.0 Min. : 0.0
1st Qu.:0 1st Qu.:0 1st Qu.: 0.0 1st Qu.: 0.0
Median :0 Median :0 Median : 0.0 Median : 0.0
Mean :0 Mean :0 Mean : 8.4 Mean : 3.7
3rd Qu.:0 3rd Qu.:0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :5 Max. :9 Max. :869.0 Max. :62.0
NA's :508595 NA's :508595 NA's :508589 NA's :508589
V145 V146 V147 V148
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0 Median : 1.0
Mean : 22.1 Mean : 0.2 Mean : 0.2 Mean : 0.8
3rd Qu.: 1.0 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 1.0
Max. :297.0 Max. :24.0 Max. :26.0 Max. :20.0
NA's :508589 NA's :508595 NA's :508595 NA's :508595
V149 V150 V151 V152
Min. : 0.0 Min. : 1.0 Min. : 1.0 Min. : 1.0
1st Qu.: 0.0 1st Qu.: 1.0 1st Qu.: 1.0 1st Qu.: 1.0
Median : 1.0 Median : 1.0 Median : 1.0 Median : 1.0
Mean : 0.8 Mean : 277.6 Mean : 6.5 Mean : 9.4
3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 1.0
Max. :20.0 Max. :3389.0 Max. :57.0 Max. :69.0
NA's :508595 NA's :508589 NA's :508589 NA's :508589
V153 V154 V155 V156
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 1.0 Median : 1.0 Median : 1.0 Median : 1.0
Mean : 0.8 Mean : 0.8 Mean : 0.8 Mean : 0.8
3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 1.0
Max. :18.0 Max. :18.0 Max. :24.0 Max. :24.0
NA's :508595 NA's :508595 NA's :508595 NA's :508595
V157 V158 V159 V160
Min. : 0.0 Min. : 0.0 Min. : 0 Min. : 0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0 1st Qu.: 0
Median : 1.0 Median : 1.0 Median : 0 Median : 0
Mean : 0.8 Mean : 0.8 Mean : 2719 Mean : 47453
3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 0 3rd Qu.: 0
Max. :24.0 Max. :24.0 Max. :55125 Max. :641511
NA's :508595 NA's :508595 NA's :508589 NA's :508589
V161 V162 V163 V164
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
Mean : 4.8 Mean : 6.6 Mean : 5.5 Mean : 877.9
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :3300.0 Max. :3300.0 Max. :3300.0 Max. :93736.0
NA's :508595 NA's :508595 NA's :508595 NA's :508589
V165 V166 V167 V168
Min. : 0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0 Median : 0.0 Median : 0.0 Median : 0.0
Mean : 2240 Mean : 359.5 Mean : 3.9 Mean : 5.9
3rd Qu.: 0 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 1.0
Max. :98476 Max. :104060.0 Max. :872.0 Max. :964.0
NA's :508589 NA's :508589 NA's :450909 NA's :450909
V169 V170 V171 V172
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 1.0 1st Qu.: 1.0 1st Qu.: 0.0
Median : 0.0 Median : 1.0 Median : 1.0 Median : 0.0
Mean : 0.2 Mean : 1.4 Mean : 1.7 Mean : 0.1
3rd Qu.: 0.0 3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 0.0
Max. :19.0 Max. :48.0 Max. :61.0 Max. :31.0
NA's :450721 NA's :450721 NA's :450721 NA's :450909
V173 V174 V175 V176
Min. :0.0 Min. :0.0 Min. : 0.0 Min. : 0.0
1st Qu.:0.0 1st Qu.:0.0 1st Qu.: 0.0 1st Qu.: 1.0
Median :0.0 Median :0.0 Median : 0.0 Median : 1.0
Mean :0.1 Mean :0.1 Mean : 0.2 Mean : 1.4
3rd Qu.:0.0 3rd Qu.:0.0 3rd Qu.: 0.0 3rd Qu.: 1.0
Max. :7.0 Max. :8.0 Max. :14.0 Max. :48.0
NA's :450909 NA's :450721 NA's :450721 NA's :450909
V177 V178 V179 V180
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
Mean : 3.5 Mean : 6.6 Mean : 4.9 Mean : 0.9
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :861.0 Max. :1235.0 Max. :920.0 Max. :83.0
NA's :450909 NA's :450909 NA's :450909 NA's :450721
V181 V182 V183 V184
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
Mean : 0.3 Mean : 0.9 Mean : 0.5 Mean : 0.1
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :24.0 Max. :83.0 Max. :41.0 Max. :16.0
NA's :450909 NA's :450909 NA's :450909 NA's :450721
V185 V186 V187 V188
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0
1st Qu.: 0.0 1st Qu.: 1.0 1st Qu.: 1.0 1st Qu.: 1
Median : 0.0 Median : 1.0 Median : 1.0 Median : 1
Mean : 0.2 Mean : 1.1 Mean : 1.8 Mean : 1
3rd Qu.: 0.0 3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 1
Max. :31.0 Max. :38.0 Max. :218.0 Max. :30
NA's :450721 NA's :450909 NA's :450909 NA's :450721
V189 V190 V191 V192
Min. : 0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 1 1st Qu.: 1.0 1st Qu.: 1.0 1st Qu.: 1.0
Median : 1 Median : 1.0 Median : 1.0 Median : 1.0
Mean : 1 Mean : 1.2 Mean : 1.1 Mean : 1.2
3rd Qu.: 1 3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 1.0
Max. :30 Max. :42.0 Max. :21.0 Max. :44.0
NA's :450721 NA's :450909 NA's :450909 NA's :450909
V193 V194 V195 V196
Min. : 0.0 Min. :0.0 Min. : 0 Min. : 0.0
1st Qu.: 1.0 1st Qu.:1.0 1st Qu.: 1 1st Qu.: 1.0
Median : 1.0 Median :1.0 Median : 1 Median : 1.0
Mean : 1.1 Mean :0.9 Mean : 1 Mean : 1.1
3rd Qu.: 1.0 3rd Qu.:1.0 3rd Qu.: 1 3rd Qu.: 1.0
Max. :37.0 Max. :7.0 Max. :16 Max. :38.0
NA's :450909 NA's :450721 NA's :450721 NA's :450909
V197 V198 V199 V200
Min. : 0.0 Min. : 0 Min. : 0.0 Min. : 0.0
1st Qu.: 1.0 1st Qu.: 1 1st Qu.: 1.0 1st Qu.: 1.0
Median : 1.0 Median : 1 Median : 1.0 Median : 1.0
Mean : 0.9 Mean : 1 Mean : 1.3 Mean : 1.1
3rd Qu.: 1.0 3rd Qu.: 1 3rd Qu.: 1.0 3rd Qu.: 1.0
Max. :14.0 Max. :21 Max. :45.0 Max. :45.0
NA's :450721 NA's :450721 NA's :450909 NA's :450721
V201 V202 V203 V204
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0
1st Qu.: 1.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0
Median : 1.0 Median : 0.0 Median : 0.0 Median : 0
Mean : 1.2 Mean : 444.1 Mean : 1078.3 Mean : 687
3rd Qu.: 1.0 3rd Qu.: 0.0 3rd Qu.: 30.9 3rd Qu.: 20
Max. :55.0 Max. :104060.0 Max. :139777.0 Max. :104060
NA's :450721 NA's :450909 NA's :450909 NA's :450909
V205 V206 V207 V208
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
Mean : 18.1 Mean : 6.2 Mean : 72.3 Mean : 8.9
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :55125.0 Max. :55125.0 Max. :55125.0 Max. :3300.0
NA's :450909 NA's :450909 NA's :450909 NA's :450721
V209 V210 V211 V212
Min. : 0 Min. : 0.0 Min. : 0.0 Min. : 0
1st Qu.: 0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0
Median : 0 Median : 0.0 Median : 0.0 Median : 0
Mean : 35 Mean : 14.4 Mean : 385.1 Mean : 766
3rd Qu.: 0 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0
Max. :8050 Max. :3300.0 Max. :92888.0 Max. :129006
NA's :450721 NA's :450721 NA's :450909 NA's :450909
V213 V214 V215
Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0
Mean : 536.3 Mean : 38.4 Mean : 133.2
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :97628.0 Max. :104060.0 Max. :104060.0
NA's :450909 NA's :450909 NA's :450909
V216 V217 V218 V219
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
Mean : 71.1 Mean : 1.1 Mean : 1.7 Mean : 1.4
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 1.0 3rd Qu.: 1.0
Max. :104060.0 Max. :303.0 Max. :400.0 Max. :378.0
NA's :450909 NA's :460110 NA's :460110 NA's :460110
V220 V221 V222 V223
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 1.0 1st Qu.: 1.0 1st Qu.: 0.0
Median : 0.0 Median : 1.0 Median : 1.0 Median : 0.0
Mean : 0.2 Mean : 1.3 Mean : 1.4 Mean : 0.1
3rd Qu.: 0.0 3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 0.0
Max. :25.0 Max. :384.0 Max. :384.0 Max. :16.0
NA's :449124 NA's :449124 NA's :449124 NA's :460110
V224 V225 V226 V227
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
Mean : 0.4 Mean : 0.2 Mean : 0.2 Mean : 0.1
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :144.0 Max. :51.0 Max. :242.0 Max. :360.0
NA's :460110 NA's :460110 NA's :460110 NA's :449124
V228 V229 V230 V231
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 1.0 1st Qu.: 1.0 1st Qu.: 1.0 1st Qu.: 0.0
Median : 1.0 Median : 1.0 Median : 1.0 Median : 0.0
Mean : 1.4 Mean : 1.6 Mean : 1.5 Mean : 0.8
3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 0.0
Max. :54.0 Max. :176.0 Max. :65.0 Max. :293.0
NA's :460110 NA's :460110 NA's :460110 NA's :460110
V232 V233 V234 V235
Min. : 0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0 Median : 0.0 Median : 0.0 Median : 0.0
Mean : 1 Mean : 0.9 Mean : 2.1 Mean : 0.2
3rd Qu.: 0 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :337 Max. :332.0 Max. :121.0 Max. :23.0
NA's :460110 NA's :460110 NA's :449124 NA's :460110
V236 V237 V238 V239
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
Mean : 0.3 Mean : 0.3 Mean : 0.1 Mean : 0.1
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :45.0 Max. :39.0 Max. :23.0 Max. :23.0
NA's :460110 NA's :460110 NA's :449124 NA's :449124
V240 V241 V242 V243
Min. :0 Min. :0 Min. : 0.0 Min. : 0.0
1st Qu.:1 1st Qu.:1 1st Qu.: 1.0 1st Qu.: 1.0
Median :1 Median :1 Median : 1.0 Median : 1.0
Mean :1 Mean :1 Mean : 1.1 Mean : 1.2
3rd Qu.:1 3rd Qu.:1 3rd Qu.: 1.0 3rd Qu.: 1.0
Max. :7 Max. :5 Max. :20.0 Max. :57.0
NA's :460110 NA's :460110 NA's :460110 NA's :460110
V244 V245 V246 V247
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0
1st Qu.: 1.0 1st Qu.: 1.0 1st Qu.: 1.0 1st Qu.: 1
Median : 1.0 Median : 1.0 Median : 1.0 Median : 1
Mean : 1.1 Mean : 0.9 Mean : 1.2 Mean : 1
3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 1
Max. :22.0 Max. :262.0 Max. :45.0 Max. :18
NA's :460110 NA's :449124 NA's :460110 NA's :460110
V248 V249 V250 V251
Min. : 0.0 Min. : 0 Min. : 0.0 Min. : 0.0
1st Qu.: 1.0 1st Qu.: 1 1st Qu.: 1.0 1st Qu.: 1.0
Median : 1.0 Median : 1 Median : 1.0 Median : 1.0
Mean : 1.1 Mean : 1 Mean : 0.8 Mean : 0.8
3rd Qu.: 1.0 3rd Qu.: 1 3rd Qu.: 1.0 3rd Qu.: 1.0
Max. :36.0 Max. :22 Max. :18.0 Max. :18.0
NA's :460110 NA's :460110 NA's :449124 NA's :449124
V252 V253 V254 V255
Min. : 0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 1 1st Qu.: 1.0 1st Qu.: 1.0 1st Qu.: 1.0
Median : 1 Median : 1.0 Median : 1.0 Median : 1.0
Mean : 1 Mean : 1.2 Mean : 1.1 Mean : 0.8
3rd Qu.: 1 3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 1.0
Max. :24 Max. :163.0 Max. :60.0 Max. :87.0
NA's :460110 NA's :460110 NA's :460110 NA's :449124
V256 V257 V258 V259
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0
1st Qu.: 1.0 1st Qu.: 1.0 1st Qu.: 1.0 1st Qu.: 1
Median : 1.0 Median : 1.0 Median : 1.0 Median : 1
Mean : 0.8 Mean : 1.3 Mean : 1.3 Mean : 1
3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 1
Max. :87.0 Max. :48.0 Max. :66.0 Max. :285
NA's :449124 NA's :460110 NA's :460110 NA's :449124
V260 V261 V262 V263
Min. :0 Min. : 0.0 Min. : 0 Min. : 0.0
1st Qu.:1 1st Qu.: 1.0 1st Qu.: 1 1st Qu.: 0.0
Median :1 Median : 1.0 Median : 1 Median : 0.0
Mean :1 Mean : 1.1 Mean : 1 Mean : 117.4
3rd Qu.:1 3rd Qu.: 1.0 3rd Qu.: 1 3rd Qu.: 0.0
Max. :8 Max. :49.0 Max. :20 Max. :153600.0
NA's :460110 NA's :460110 NA's :460110 NA's :460110
V264 V265 V266 V267
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
Mean : 201.7 Mean : 153.5 Mean : 9.2 Mean : 36.5
3rd Qu.: 33.6 3rd Qu.: 20.9 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :153600.0 Max. :153600.0 Max. :55125.0 Max. :55125.0
NA's :460110 NA's :460110 NA's :460110 NA's :460110
V268 V269 V270 V271
Min. : 0.0 Min. : 0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0 Median : 0.0 Median : 0.0
Mean : 18.8 Mean : 6 Mean : 7.7 Mean : 9.4
3rd Qu.: 0.0 3rd Qu.: 0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :55125.0 Max. :55125 Max. :4000.0 Max. :4000.0
NA's :460110 NA's :460110 NA's :449124 NA's :449124
V272 V273 V274 V275
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
Mean : 8.5 Mean : 73.8 Mean : 107.2 Mean : 88.9
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :4000.0 Max. :51200.0 Max. :66000.0 Max. :51200.0
NA's :449124 NA's :460110 NA's :460110 NA's :460110
V276 V277 V278 V279
Min. : 0.0 Min. : 0 Min. : 0.0 Min. : 0.000
1st Qu.: 0.0 1st Qu.: 0 1st Qu.: 0.0 1st Qu.: 0.000
Median : 0.0 Median : 0 Median : 0.0 Median : 0.000
Mean : 31.8 Mean : 52 Mean : 42.3 Mean : 1.123
3rd Qu.: 0.0 3rd Qu.: 0 3rd Qu.: 0.0 3rd Qu.: 0.000
Max. :104060.0 Max. :104060 Max. :104060.0 Max. :880.000
NA's :460110 NA's :460110 NA's :460110 NA's :12
V280 V281 V282 V283
Min. : 0.000 Min. : 0.0000 Min. : 0.0000 Min. : 0.0000
1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.0000
Median : 0.000 Median : 0.0000 Median : 1.0000 Median : 1.0000
Mean : 1.967 Mean : 0.0878 Mean : 0.8172 Mean : 0.9911
3rd Qu.: 1.000 3rd Qu.: 0.0000 3rd Qu.: 1.0000 3rd Qu.: 1.0000
Max. :975.000 Max. :22.0000 Max. :32.0000 Max. :68.0000
NA's :12 NA's :1269 NA's :1269 NA's :1269
V284 V285 V286 V287
Min. : 0.00000 Min. : 0.000 Min. :0.00000 Min. : 0.0000
1st Qu.: 0.00000 1st Qu.: 0.000 1st Qu.:0.00000 1st Qu.: 0.0000
Median : 0.00000 Median : 0.000 Median :0.00000 Median : 0.0000
Mean : 0.08854 Mean : 1.168 Mean :0.03149 Mean : 0.3586
3rd Qu.: 0.00000 3rd Qu.: 1.000 3rd Qu.:0.00000 3rd Qu.: 0.0000
Max. :12.00000 Max. :95.000 Max. :8.00000 Max. :31.0000
NA's :12 NA's :12 NA's :12 NA's :12
V288 V289 V290 V291
Min. : 0.0000 Min. : 0.000 Min. : 1.000 Min. : 1.00
1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 1.000 1st Qu.: 1.00
Median : 0.0000 Median : 0.000 Median : 1.000 Median : 1.00
Mean : 0.1843 Mean : 0.236 Mean : 1.103 Mean : 1.66
3rd Qu.: 0.0000 3rd Qu.: 0.000 3rd Qu.: 1.000 3rd Qu.: 1.00
Max. :10.0000 Max. :12.000 Max. :67.000 Max. :1055.00
NA's :1269 NA's :1269 NA's :12 NA's :12
V292 V293 V294 V295
Min. : 1.00 Min. : 0.0000 Min. : 0.000 Min. : 0.000
1st Qu.: 1.00 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000
Median : 1.00 Median : 0.0000 Median : 0.000 Median : 0.000
Mean : 1.24 Mean : 0.9426 Mean : 2.314 Mean : 1.433
3rd Qu.: 1.00 3rd Qu.: 0.0000 3rd Qu.: 0.000 3rd Qu.: 0.000
Max. :323.00 Max. :869.0000 Max. :1286.000 Max. :928.000
NA's :12 NA's :12 NA's :12 NA's :12
V296 V297 V298 V299
Min. : 0.0000 Min. : 0.00000 Min. : 0.0000 Min. : 0.0000
1st Qu.: 0.0000 1st Qu.: 0.00000 1st Qu.: 0.0000 1st Qu.: 0.0000
Median : 0.0000 Median : 0.00000 Median : 0.0000 Median : 0.0000
Mean : 0.3289 Mean : 0.08903 Mean : 0.2988 Mean : 0.1716
3rd Qu.: 0.0000 3rd Qu.: 0.00000 3rd Qu.: 0.0000 3rd Qu.: 0.0000
Max. :93.0000 Max. :12.00000 Max. :93.0000 Max. :49.0000
NA's :1269 NA's :12 NA's :12 NA's :12
V300 V301 V302 V303
Min. : 0.0000 Min. : 0.000 Min. : 0.0000 Min. : 0.0000
1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.0000
Median : 0.0000 Median : 0.000 Median : 0.0000 Median : 0.0000
Mean : 0.0455 Mean : 0.052 Mean : 0.2518 Mean : 0.2831
3rd Qu.: 0.0000 3rd Qu.: 0.000 3rd Qu.: 0.0000 3rd Qu.: 0.0000
Max. :11.0000 Max. :13.000 Max. :16.0000 Max. :20.0000
NA's :1269 NA's :1269 NA's :12 NA's :12
V304 V305 V306 V307
Min. : 0.0000 Min. :1 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0000 1st Qu.:1 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0000 Median :1 Median : 0.0 Median : 0.0
Mean : 0.2642 Mean :1 Mean : 139.8 Mean : 408.7
3rd Qu.: 0.0000 3rd Qu.:1 3rd Qu.: 0.0 3rd Qu.: 151.4
Max. :16.0000 Max. :2 Max. :108800.0 Max. :145765.0
NA's :12 NA's :12 NA's :12 NA's :12
V308 V309 V310 V311
Min. : 0.00 Min. : 0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.00 1st Qu.: 0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.00 Median : 0 Median : 0.0 Median : 0.0
Mean : 230.41 Mean : 11 Mean : 118.2 Mean : 4.2
3rd Qu.: 35.97 3rd Qu.: 0 3rd Qu.: 108.0 3rd Qu.: 0.0
Max. :108800.00 Max. :55125 Max. :55125.0 Max. :55125.0
NA's :12 NA's :12 NA's :12 NA's :12
V312 V313 V314 V315
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00
Median : 0.00 Median : 0.00 Median : 0.00 Median : 0.00
Mean : 39.17 Mean : 21.35 Mean : 43.32 Mean : 26.81
3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.: 0.00
Max. :55125.00 Max. :4817.47 Max. :7519.87 Max. :4817.47
NA's :12 NA's :1269 NA's :1269 NA's :1269
V316 V317 V318
Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0
Mean : 109.8 Mean : 247.6 Mean : 162.2
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :93736.0 Max. :134021.0 Max. :98476.0
NA's :12 NA's :12 NA's :12
V319 V320 V321
Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00
Median : 0.00 Median : 0.00 Median : 0.00
Mean : 18.37 Mean : 42.07 Mean : 28.33
3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.: 0.00
Max. :104060.00 Max. :104060.00 Max. :104060.00
NA's :12 NA's :12 NA's :12
V322 V323 V324 V325
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
Mean : 6.2 Mean : 13.1 Mean : 9.2 Mean : 0.1
3rd Qu.: 0.0 3rd Qu.: 1.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :880.0 Max. :1411.0 Max. :976.0 Max. :12.0
NA's :508189 NA's :508189 NA's :508189 NA's :508189
V326 V327 V328 V329
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
Mean : 0.9 Mean : 0.3 Mean : 0.3 Mean : 1.3
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :44.0 Max. :18.0 Max. :15.0 Max. :99.0
NA's :508189 NA's :508189 NA's :508189 NA's :508189
V330 V331 V332 V333
Min. : 0.0 Min. : 0.0 Min. : 0 Min. : 0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0 1st Qu.: 0
Median : 0.0 Median : 0.0 Median : 0 Median : 0
Mean : 0.8 Mean : 721.7 Mean : 1376 Mean : 1015
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 25 3rd Qu.: 0
Max. :55.0 Max. :160000.0 Max. :160000 Max. :160000
NA's :508189 NA's :508189 NA's :508189 NA's :508189
V334 V335 V336 V337
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
Mean : 9.8 Mean : 59.2 Mean : 28.5 Mean : 55.4
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :55125.0 Max. :55125.0 Max. :55125.0 Max. :104060.0
NA's :508189 NA's :508189 NA's :508189 NA's :508189
V338 V339
Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0
Mean : 151.2 Mean : 100.7
3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :104060.0 Max. :104060.0
NA's :508189 NA's :508189 test_t
str(test_t)'data.frame': 506691 obs. of 393 variables:
$ TransactionID : int 3663549 3663550 3663551 3663552 3663553 3663554 3663555 3663556 3663557 3663558 ...
$ TransactionDT : int 18403224 18403263 18403310 18403310 18403317 18403323 18403350 18403387 18403405 18403416 ...
$ TransactionAmt: num 31.9 49 171 284.9 68 ...
$ ProductCD : Factor w/ 5 levels "C","H","R","S",..: 5 5 5 5 5 5 5 5 5 5 ...
$ card1 : int 10409 4272 4476 10989 18018 12839 16560 15066 2803 12544 ...
$ card2 : num 111 111 574 360 452 321 476 170 100 321 ...
$ card3 : num 150 150 150 150 150 150 150 150 150 150 ...
$ card4 : Factor w/ 4 levels "american express",..: 4 4 4 4 3 4 4 3 4 4 ...
$ card5 : num 226 226 226 166 117 226 126 102 226 226 ...
$ card6 : Factor w/ 3 levels "charge card",..: 3 3 3 3 3 3 3 2 3 3 ...
$ addr1 : num 170 299 472 205 264 512 110 194 494 476 ...
$ addr2 : num 87 87 87 87 87 87 87 87 87 87 ...
$ dist1 : num 1 4 2635 17 6 ...
$ dist2 : num NA NA NA NA NA NA NA NA NA NA ...
$ P_emaildomain : Factor w/ 60 levels "aim.com","anonymous.com",..: 17 3 20 17 17 17 17 17 17 NA ...
$ R_emaildomain : Factor w/ 60 levels "aim.com","anonymous.com",..: NA NA NA NA NA NA NA NA NA NA ...
$ C1 : num 6 3 2 5 6 5 1 3 152 2 ...
$ C2 : num 6 2 2 2 6 5 1 1 148 2 ...
$ C3 : num 0 0 0 0 0 0 0 0 0 0 ...
$ C4 : num 0 0 0 0 0 0 0 0 0 0 ...
$ C5 : num 3 0 0 1 2 2 0 0 135 0 ...
$ C6 : num 4 1 5 1 5 3 1 1 95 1 ...
$ C7 : num 0 0 0 0 0 0 0 0 0 0 ...
$ C8 : num 0 0 0 0 0 0 0 0 0 0 ...
$ C9 : num 6 2 4 2 5 2 1 3 77 2 ...
$ C10 : num 0 0 0 0 0 0 0 0 0 0 ...
$ C11 : num 5 1 2 2 6 4 1 1 122 2 ...
$ C12 : num 1 1 0 0 0 0 0 1 0 1 ...
$ C13 : num 115 12 22 7 14 10 2 11 407 8 ...
$ C14 : num 6 2 2 4 6 4 1 1 108 2 ...
$ D1 : num 419 149 137 42 22 36 NA NA 128 69 ...
$ D2 : num 419 149 137 42 22 36 NA NA 128 69 ...
$ D3 : num 27 7 10 41 0 35 0 NA 13 35 ...
$ D4 : num 398 634 97 242 22 0 0 126 644 NA ...
$ D5 : num 27 7 10 41 0 NA NA 4 13 NA ...
$ D6 : num NA NA NA NA NA NA NA NA NA NA ...
$ D7 : num NA NA NA NA NA NA NA NA NA NA ...
$ D8 : num NA NA NA NA NA NA NA NA NA NA ...
$ D9 : num NA NA NA NA NA NA NA NA NA NA ...
$ D10 : num 418 231 136 242 22 0 0 126 106 68 ...
$ D11 : num 203 634 136 242 22 0 0 126 631 68 ...
$ D12 : num NA NA NA NA NA NA NA NA NA NA ...
$ D13 : num NA NA NA NA NA NA NA NA NA NA ...
$ D14 : num NA NA NA NA NA NA NA NA NA NA ...
$ D15 : num 409 634 97 242 22 0 0 126 673 43 ...
$ M1 : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
$ M2 : logi TRUE FALSE TRUE TRUE TRUE TRUE ...
$ M3 : logi FALSE FALSE FALSE TRUE TRUE TRUE ...
$ M4 : Factor w/ 3 levels "M0","M1","M2": NA 1 1 NA NA NA 2 1 NA 2 ...
$ M5 : logi NA NA FALSE NA NA NA ...
$ M6 : logi FALSE FALSE FALSE TRUE FALSE TRUE ...
$ M7 : logi TRUE NA FALSE NA FALSE FALSE ...
$ M8 : logi TRUE NA FALSE NA TRUE FALSE ...
$ M9 : logi TRUE NA FALSE NA TRUE TRUE ...
$ V1 : num 1 1 1 1 1 1 1 1 1 1 ...
$ V2 : num 1 1 1 1 1 1 1 1 1 1 ...
$ V3 : num 1 1 1 1 1 1 1 1 1 1 ...
$ V4 : num 1 1 1 1 1 1 1 1 1 1 ...
$ V5 : num 1 1 1 1 1 1 1 1 1 1 ...
$ V6 : num 1 1 1 1 1 1 1 1 1 1 ...
$ V7 : num 1 1 1 1 1 1 1 1 1 1 ...
$ V8 : num 1 1 1 1 1 1 1 1 1 1 ...
$ V9 : num 1 1 1 1 1 1 1 1 1 1 ...
$ V10 : num 1 0 0 1 1 1 0 0 1 0 ...
$ V11 : num 1 0 0 1 1 1 0 0 1 0 ...
$ V12 : num 0 1 1 1 1 1 1 1 1 1 ...
$ V13 : num 0 1 1 1 1 1 1 1 1 1 ...
$ V14 : num 1 1 1 1 1 1 1 1 1 1 ...
$ V15 : num 0 0 0 0 0 0 0 0 0 0 ...
$ V16 : num 0 0 0 0 0 0 0 0 0 0 ...
$ V17 : num 0 0 0 0 0 0 0 0 0 0 ...
$ V18 : num 0 0 0 0 0 0 0 0 0 0 ...
$ V19 : num 0 1 1 1 1 1 1 1 1 1 ...
$ V20 : num 0 1 1 1 1 1 1 1 1 1 ...
$ V21 : num 0 0 0 0 0 0 0 0 0 0 ...
$ V22 : num 0 0 0 0 0 0 0 0 0 0 ...
$ V23 : num 1 1 2 1 1 1 1 1 1 1 ...
$ V24 : num 1 1 2 1 1 1 1 1 1 1 ...
$ V25 : num 0 1 1 1 1 1 1 1 1 1 ...
$ V26 : num 0 1 1 1 1 1 1 1 1 1 ...
$ V27 : num 0 0 0 0 0 0 0 0 0 0 ...
$ V28 : num 0 0 0 0 0 0 0 0 0 0 ...
$ V29 : num 0 0 0 1 1 1 0 0 2 0 ...
$ V30 : num 0 0 0 1 1 1 0 0 2 0 ...
$ V31 : num 0 0 0 0 0 0 0 0 0 0 ...
$ V32 : num 0 0 0 0 0 0 0 0 0 0 ...
$ V33 : num 0 0 0 0 0 0 0 0 0 0 ...
$ V34 : num 0 0 0 0 0 0 0 1 0 0 ...
$ V35 : num 1 1 1 2 1 1 1 1 0 NA ...
$ V36 : num 1 1 1 2 1 1 1 1 0 NA ...
$ V37 : num 1 1 1 2 1 1 1 1 1 NA ...
$ V38 : num 1 1 1 2 1 1 1 1 1 NA ...
$ V39 : num 0 0 0 0 0 0 0 0 0 NA ...
$ V40 : num 0 0 0 0 0 0 0 0 0 NA ...
$ V41 : num 1 1 1 1 1 1 1 1 1 NA ...
$ V42 : num 0 0 0 0 0 0 0 0 0 NA ...
$ V43 : num 0 0 0 0 0 0 0 0 0 NA ...
$ V44 : num 1 1 1 2 1 1 1 1 1 NA ...
$ V45 : num 1 1 1 2 1 1 1 1 1 NA ...
[list output truncated]summary(test_t) TransactionID TransactionDT TransactionAmt ProductCD
Min. :3663549 Min. :18403224 Min. : 0.018 C: 69266
1st Qu.:3790222 1st Qu.:22771540 1st Qu.: 40.000 H: 29373
Median :3916894 Median :27204658 Median : 67.950 R: 35647
Mean :3916894 Mean :26929937 Mean : 134.726 S: 11418
3rd Qu.:4043566 3rd Qu.:31348560 3rd Qu.: 125.000 W:360987
Max. :4170239 Max. :34214345 Max. :10270.000
card1 card2 card3 card4
Min. : 1001 Min. :100.0 Min. :100.0 american express: 7681
1st Qu.: 6019 1st Qu.:207.0 1st Qu.:150.0 discover : 2873
Median : 9803 Median :369.0 Median :150.0 mastercard :158169
Mean : 9957 Mean :363.7 Mean :153.5 visa :334882
3rd Qu.:14276 3rd Qu.:512.0 3rd Qu.:150.0 NA's : 3086
Max. :18397 Max. :600.0 Max. :232.0
NA's :8654 NA's :3002
card5 card6 addr1 addr2
Min. :100.0 charge card: 1 Min. :100.0 Min. : 10.00
1st Qu.:166.0 credit :118662 1st Qu.:204.0 1st Qu.: 87.00
Median :226.0 debit :385021 Median :299.0 Median : 87.00
Mean :200.2 NA's : 3007 Mean :291.9 Mean : 86.72
3rd Qu.:226.0 3rd Qu.:330.0 3rd Qu.: 87.00
Max. :237.0 Max. :540.0 Max. :102.00
NA's :4547 NA's :65609 NA's :65609
dist1 dist2 P_emaildomain
Min. : 0.00 Min. : 0.0 gmail.com :207448
1st Qu.: 3.00 1st Qu.: 7.0 yahoo.com : 81850
Median : 8.00 Median : 44.0 hotmail.com : 40399
Mean : 87.07 Mean : 237.2 anonymous.com: 34064
3rd Qu.: 20.00 3rd Qu.: 196.0 aol.com : 24048
Max. :8081.00 Max. :9213.0 (Other) : 49690
NA's :291217 NA's :470255 NA's : 69192
R_emaildomain C1 C2
gmail.com : 61738 Min. : 0.00 Min. : 0.00
hotmail.com : 25657 1st Qu.: 1.00 1st Qu.: 1.00
anonymous.com: 19115 Median : 1.00 Median : 1.00
yahoo.com : 9563 Mean : 10.09 Mean : 10.71
aol.com : 3538 3rd Qu.: 3.00 3rd Qu.: 3.00
(Other) : 16259 Max. :2950.00 Max. :3275.00
NA's :370821 NA's :3 NA's :3
C3 C4 C5 C6
Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000
1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 1.000
Median : 0.0000 Median : 0.000 Median : 0.000 Median : 1.000
Mean : 0.0274 Mean : 2.386 Mean : 4.963 Mean : 6.855
3rd Qu.: 0.0000 3rd Qu.: 1.000 3rd Qu.: 1.000 3rd Qu.: 2.000
Max. :31.0000 Max. :1601.000 Max. :376.000 Max. :1601.000
NA's :3 NA's :3 NA's :3 NA's :3
C7 C8 C9 C10
Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.00
1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.00
Median : 0.000 Median : 0.000 Median : 1.000 Median : 0.00
Mean : 1.678 Mean : 1.894 Mean : 4.612 Mean : 1.81
3rd Qu.: 0.000 3rd Qu.: 1.000 3rd Qu.: 2.000 3rd Qu.: 1.00
Max. :1621.000 Max. :1005.000 Max. :572.000 Max. :881.00
NA's :3 NA's :3 NA's :3 NA's :3
C11 C12 C13 C14
Min. : 0.000 Min. : 0.00 Min. : 0.00 Min. : 0.000
1st Qu.: 1.000 1st Qu.: 0.00 1st Qu.: 1.00 1st Qu.: 1.000
Median : 1.000 Median : 0.00 Median : 3.00 Median : 1.000
Mean : 7.485 Mean : 2.65 Mean : 27.82 Mean : 6.084
3rd Qu.: 2.000 3rd Qu.: 1.00 3rd Qu.: 13.00 3rd Qu.: 2.000
Max. :2234.000 Max. :2234.00 Max. :1562.00 Max. :797.000
NA's :3 NA's :3 NA's :4748 NA's :3
D1 D2 D3 D4
Min. : 0.0 Min. : 0.0 Min. : 0.00 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 26.0 1st Qu.: 1.00 1st Qu.: 0.0
Median : 5.0 Median :112.0 Median : 7.00 Median : 21.0
Mean :108.2 Mean :188.7 Mean : 33.39 Mean : 175.1
3rd Qu.:148.0 3rd Qu.:305.0 3rd Qu.: 28.00 3rd Qu.: 290.0
Max. :641.0 Max. :641.0 Max. :1076.00 Max. :1091.0
NA's :6031 NA's :234769 NA's :203142 NA's :76851
D5 D6 D7 D8
Min. : 0.00 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.00 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 1.1
Median : 8.00 Median : 0.0 Median : 0.0 Median : 37.7
Mean : 50.98 Mean : 82.4 Mean : 61.8 Mean : 160.8
3rd Qu.: 34.00 3rd Qu.: 13.0 3rd Qu.: 18.0 3rd Qu.: 221.1
Max. :1088.00 Max. :1091.0 Max. :1088.0 Max. :2029.6
NA's :224375 NA's :381908 NA's :446558 NA's :432353
D9 D10 D11 D12
Min. :0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.:0.2 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median :0.7 Median : 10.0 Median :102.0 Median : 0.0
Mean :0.6 Mean : 159.8 Mean :218.4 Mean : 77.4
3rd Qu.:0.8 3rd Qu.: 250.0 3rd Qu.:401.0 3rd Qu.: 25.0
Max. :1.0 Max. :1091.0 Max. :883.0 Max. :879.0
NA's :432353 NA's :12545 NA's :176518 NA's :437437
D13 D14 D15 M1
Min. : 0.0 Min. : 0.0 Min. : 0.0 Mode :logical
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 FALSE:31
Median : 0.0 Median : 0.0 Median : 48.0 TRUE :330021
Mean : 18.2 Mean : 58.2 Mean : 206.9 NA's :176639
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 370.0
Max. :1066.0 Max. :1085.0 Max. :1091.0
NA's :383307 NA's :391497 NA's :12069
M2 M3 M4 M5
Mode :logical Mode :logical M0 :161384 Mode :logical
FALSE:27197 FALSE:63539 M1 : 44480 FALSE:107664
TRUE :302855 TRUE :266513 M2 : 63082 TRUE :89395
NA's :176639 NA's :176639 NA's:237745 NA's :309632
M6 M7 M8 M9
Mode :logical Mode :logical Mode :logical Mode :logical
FALSE:191577 FALSE:233230 FALSE:168399 FALSE:35408
TRUE :156175 TRUE :38443 TRUE :103288 TRUE :236279
NA's :158939 NA's :235018 NA's :235004 NA's :235004
V1 V2 V3 V4
Min. :0 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.:1 1st Qu.: 1.00 1st Qu.: 1.00 1st Qu.: 1.00
Median :1 Median : 1.00 Median : 1.00 Median : 1.00
Mean :1 Mean : 1.05 Mean : 1.09 Mean : 0.85
3rd Qu.:1 3rd Qu.: 1.00 3rd Qu.: 1.00 3rd Qu.: 1.00
Max. :1 Max. :11.00 Max. :11.00 Max. :10.00
NA's :176518 NA's :176518 NA's :176518 NA's :176518
V5 V6 V7 V8
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 1.00 1st Qu.: 1.00 1st Qu.: 1.00 1st Qu.: 1.00
Median : 1.00 Median : 1.00 Median : 1.00 Median : 1.00
Mean : 0.88 Mean : 1.05 Mean : 1.08 Mean : 1.02
3rd Qu.: 1.00 3rd Qu.: 1.00 3rd Qu.: 1.00 3rd Qu.: 1.00
Max. :10.00 Max. :13.00 Max. :13.00 Max. :11.00
NA's :176518 NA's :176518 NA's :176518 NA's :176518
V9 V10 V11 V12
Min. : 0.00 Min. :0.00 Min. :0.00 Min. :0.00
1st Qu.: 1.00 1st Qu.:0.00 1st Qu.:0.00 1st Qu.:0.00
Median : 1.00 Median :0.00 Median :0.00 Median :1.00
Mean : 1.04 Mean :0.46 Mean :0.47 Mean :0.57
3rd Qu.: 1.00 3rd Qu.:1.00 3rd Qu.:1.00 3rd Qu.:1.00
Max. :11.00 Max. :5.00 Max. :7.00 Max. :4.00
NA's :176518 NA's :176518 NA's :176518 NA's :12589
V13 V14 V15 V16
Min. :0.0 Min. :0 Min. : 0.000 Min. : 0.000
1st Qu.:0.0 1st Qu.:1 1st Qu.: 0.000 1st Qu.: 0.000
Median :1.0 Median :1 Median : 0.000 Median : 0.000
Mean :0.6 Mean :1 Mean : 0.141 Mean : 0.142
3rd Qu.:1.0 3rd Qu.:1 3rd Qu.: 0.000 3rd Qu.: 0.000
Max. :6.0 Max. :1 Max. :13.000 Max. :25.000
NA's :12589 NA's :12589 NA's :12589 NA's :12589
V17 V18 V19 V20
Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. : 0.000
1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 1.000 1st Qu.: 1.000
Median : 0.00 Median : 0.000 Median : 1.000 Median : 1.000
Mean : 0.26 Mean : 0.262 Mean : 0.861 Mean : 0.886
3rd Qu.: 1.00 3rd Qu.: 1.000 3rd Qu.: 1.000 3rd Qu.: 1.000
Max. :10.00 Max. :10.000 Max. :13.000 Max. :25.000
NA's :12589 NA's :12589 NA's :12589 NA's :12589
V21 V22 V23 V24
Min. :0.00 Min. :0.000 Min. : 0.000 Min. : 0.000
1st Qu.:0.00 1st Qu.:0.000 1st Qu.: 1.000 1st Qu.: 1.000
Median :0.00 Median :0.000 Median : 1.000 Median : 1.000
Mean :0.25 Mean :0.254 Mean : 1.045 Mean : 1.069
3rd Qu.:0.00 3rd Qu.:0.000 3rd Qu.: 1.000 3rd Qu.: 1.000
Max. :5.00 Max. :7.000 Max. :15.000 Max. :29.000
NA's :12589 NA's :12589 NA's :12589 NA's :12589
V25 V26 V27 V28
Min. : 0.000 Min. : 0.000 Min. :0.000 Min. :0.000
1st Qu.: 1.000 1st Qu.: 1.000 1st Qu.:0.000 1st Qu.:0.000
Median : 1.000 Median : 1.000 Median :0.000 Median :0.000
Mean : 1.005 Mean : 1.013 Mean :0.023 Mean :0.023
3rd Qu.: 1.000 3rd Qu.: 1.000 3rd Qu.:0.000 3rd Qu.:0.000
Max. :13.000 Max. :25.000 Max. :7.000 Max. :7.000
NA's :12589 NA's :12589 NA's :12589 NA's :12589
V29 V30 V31 V32
Min. :0.000 Min. :0.000 Min. : 0.000 Min. : 0.000
1st Qu.:0.000 1st Qu.:0.000 1st Qu.: 0.000 1st Qu.: 0.000
Median :0.000 Median :0.000 Median : 0.000 Median : 0.000
Mean :0.337 Mean :0.352 Mean : 0.256 Mean : 0.258
3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.: 1.000 3rd Qu.: 1.000
Max. :4.000 Max. :7.000 Max. :13.000 Max. :25.000
NA's :12589 NA's :12589 NA's :12589 NA's :12589
V33 V34 V35 V36
Min. : 0.000 Min. : 0.00 Min. :0.00 Min. :0.00
1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.:0.00 1st Qu.:0.00
Median : 0.000 Median : 0.00 Median :1.00 Median :1.00
Mean : 0.155 Mean : 0.17 Mean :0.54 Mean :0.57
3rd Qu.: 0.000 3rd Qu.: 0.00 3rd Qu.:1.00 3rd Qu.:1.00
Max. :13.000 Max. :25.00 Max. :4.00 Max. :6.00
NA's :12589 NA's :12589 NA's :76854 NA's :76854
V37 V38 V39 V40
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 1.00 1st Qu.: 1.00 1st Qu.: 0.00 1st Qu.: 0.00
Median : 1.00 Median : 1.00 Median : 0.00 Median : 0.00
Mean : 1.14 Mean : 1.21 Mean : 0.29 Mean : 0.31
3rd Qu.: 1.00 3rd Qu.: 1.00 3rd Qu.: 1.00 3rd Qu.: 1.00
Max. :49.00 Max. :52.00 Max. :30.00 Max. :31.00
NA's :76854 NA's :76854 NA's :76854 NA's :76854
V41 V42 V43 V44
Min. :0 Min. :0.00 Min. : 0.0 Min. : 0.0
1st Qu.:1 1st Qu.:0.00 1st Qu.: 0.0 1st Qu.: 1.0
Median :1 Median :0.00 Median : 0.0 Median : 1.0
Mean :1 Mean :0.28 Mean : 0.3 Mean : 1.1
3rd Qu.:1 3rd Qu.:1.00 3rd Qu.: 1.0 3rd Qu.: 1.0
Max. :1 Max. :8.00 Max. :11.0 Max. :47.0
NA's :76854 NA's :76854 NA's :76854 NA's :76854
V45 V46 V47 V48
Min. : 0.00 Min. :0.00 Min. : 0.00 Min. :0.00
1st Qu.: 1.00 1st Qu.:1.00 1st Qu.: 1.00 1st Qu.:0.00
Median : 1.00 Median :1.00 Median : 1.00 Median :0.00
Mean : 1.15 Mean :1.02 Mean : 1.04 Mean :0.33
3rd Qu.: 1.00 3rd Qu.:1.00 3rd Qu.: 1.00 3rd Qu.:1.00
Max. :69.00 Max. :8.00 Max. :11.00 Max. :4.00
NA's :76854 NA's :76854 NA's :76854 NA's :76854
V49 V50 V51 V52
Min. :0.00 Min. :0.00 Min. :0.00 Min. : 0.00
1st Qu.:0.00 1st Qu.:0.00 1st Qu.:0.00 1st Qu.: 0.00
Median :0.00 Median :0.00 Median :0.00 Median : 0.00
Mean :0.34 Mean :0.27 Mean :0.19 Mean : 0.21
3rd Qu.:1.00 3rd Qu.:1.00 3rd Qu.:0.00 3rd Qu.: 0.00
Max. :7.00 Max. :7.00 Max. :8.00 Max. :10.00
NA's :76854 NA's :76854 NA's :76854 NA's :76854
V53 V54 V55 V56
Min. :0.000 Min. :0.000 Min. : 0.000 Min. : 0.000
1st Qu.:0.000 1st Qu.:0.000 1st Qu.: 1.000 1st Qu.: 1.000
Median :1.000 Median :1.000 Median : 1.000 Median : 1.000
Mean :0.605 Mean :0.634 Mean : 1.093 Mean : 1.156
3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.: 1.000 3rd Qu.: 1.000
Max. :5.000 Max. :7.000 Max. :49.000 Max. :51.000
NA's :12899 NA's :12899 NA's :12899 NA's :12899
V57 V58 V59 V60
Min. :0.000 Min. : 0.000 Min. : 0.000 Min. : 0.00
1st Qu.:0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.00
Median :0.000 Median : 0.000 Median : 0.000 Median : 0.00
Mean :0.146 Mean : 0.151 Mean : 0.266 Mean : 0.28
3rd Qu.:0.000 3rd Qu.: 0.000 3rd Qu.: 1.000 3rd Qu.: 1.00
Max. :6.000 Max. :10.000 Max. :12.000 Max. :17.00
NA's :12899 NA's :12899 NA's :12899 NA's :12899
V61 V62 V63 V64
Min. :0.00 Min. : 0.000 Min. :0.000 Min. : 0.00
1st Qu.:1.00 1st Qu.: 1.000 1st Qu.:0.000 1st Qu.: 0.00
Median :1.00 Median : 1.000 Median :0.000 Median : 0.00
Mean :0.88 Mean : 0.913 Mean :0.257 Mean : 0.28
3rd Qu.:1.00 3rd Qu.: 1.000 3rd Qu.:0.000 3rd Qu.: 0.00
Max. :6.00 Max. :10.000 Max. :8.000 Max. :10.00
NA's :12899 NA's :12899 NA's :12899 NA's :12899
V65 V66 V67 V68
Min. :0 Min. :0.000 Min. : 0.000 Min. :0.000
1st Qu.:1 1st Qu.:1.000 1st Qu.: 1.000 1st Qu.:0.000
Median :1 Median :1.000 Median : 1.000 Median :0.000
Mean :1 Mean :1.015 Mean : 1.032 Mean :0.023
3rd Qu.:1 3rd Qu.:1.000 3rd Qu.: 1.000 3rd Qu.:0.000
Max. :1 Max. :8.000 Max. :10.000 Max. :7.000
NA's :12899 NA's :12899 NA's :12899 NA's :12899
V69 V70 V71 V72
Min. :0.000 Min. :0.000 Min. :0.000 Min. : 0.000
1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.: 0.000
Median :0.000 Median :0.000 Median :0.000 Median : 0.000
Mean :0.339 Mean :0.354 Mean :0.259 Mean : 0.265
3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.: 1.000
Max. :4.000 Max. :8.000 Max. :6.000 Max. :10.000
NA's :12899 NA's :12899 NA's :12899 NA's :12899
V73 V74 V75 V76
Min. :0.000 Min. : 0.000 Min. :0.000 Min. :0.000
1st Qu.:0.000 1st Qu.: 0.000 1st Qu.:0.000 1st Qu.:0.000
Median :0.000 Median : 0.000 Median :1.000 Median :1.000
Mean :0.162 Mean : 0.186 Mean :0.539 Mean :0.574
3rd Qu.:0.000 3rd Qu.: 0.000 3rd Qu.:1.000 3rd Qu.:1.000
Max. :8.000 Max. :10.000 Max. :5.000 Max. :7.000
NA's :12899 NA's :12899 NA's :12081 NA's :12081
V77 V78 V79 V80
Min. : 0.000 Min. : 0.000 Min. :0.000 Min. : 0.00
1st Qu.: 1.000 1st Qu.: 1.000 1st Qu.:0.000 1st Qu.: 0.00
Median : 1.000 Median : 1.000 Median :0.000 Median : 0.00
Mean : 1.124 Mean : 1.191 Mean :0.151 Mean : 0.27
3rd Qu.: 1.000 3rd Qu.: 1.000 3rd Qu.:0.000 3rd Qu.: 1.00
Max. :80.000 Max. :80.000 Max. :7.000 Max. :18.00
NA's :12081 NA's :12081 NA's :12081 NA's :12081
V81 V82 V83 V84
Min. : 0.000 Min. :0.000 Min. :0.000 Min. : 0.000
1st Qu.: 0.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.: 0.000
Median : 0.000 Median :1.000 Median :1.000 Median : 0.000
Mean : 0.285 Mean :0.864 Mean :0.897 Mean : 0.258
3rd Qu.: 1.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.: 0.000
Max. :18.000 Max. :5.000 Max. :7.000 Max. :10.000
NA's :12081 NA's :12081 NA's :12081 NA's :12081
V85 V86 V87 V88
Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. :0
1st Qu.: 0.00 1st Qu.: 1.000 1st Qu.: 1.000 1st Qu.:1
Median : 0.00 Median : 1.000 Median : 1.000 Median :1
Mean : 0.28 Mean : 1.092 Mean : 1.132 Mean :1
3rd Qu.: 0.00 3rd Qu.: 1.000 3rd Qu.: 1.000 3rd Qu.:1
Max. :10.00 Max. :80.000 Max. :80.000 Max. :1
NA's :12081 NA's :12081 NA's :12081 NA's :12081
V89 V90 V91 V92
Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.00
1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.00
Median :0.000 Median :0.000 Median :0.000 Median :0.00
Mean :0.023 Mean :0.341 Mean :0.357 Mean :0.26
3rd Qu.:0.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.00
Max. :7.000 Max. :4.000 Max. :8.000 Max. :6.00
NA's :12081 NA's :12081 NA's :12081 NA's :12081
V93 V94 V95 V96
Min. :0.000 Min. :0.000 Min. : 0.0000 Min. : 0.000
1st Qu.:0.000 1st Qu.:0.000 1st Qu.: 0.0000 1st Qu.: 0.000
Median :0.000 Median :0.000 Median : 0.0000 Median : 0.000
Mean :0.267 Mean :0.153 Mean : 0.2317 Mean : 1.377
3rd Qu.:1.000 3rd Qu.:0.000 3rd Qu.: 0.0000 3rd Qu.: 1.000
Max. :7.000 Max. :2.000 Max. :60.0000 Max. :103.000
NA's :12081 NA's :12081
V97 V98 V99 V100
Min. : 0.0000 Min. :0.00000 Min. : 0.00 Min. : 0.0000
1st Qu.: 0.0000 1st Qu.:0.00000 1st Qu.: 0.00 1st Qu.: 0.0000
Median : 0.0000 Median :0.00000 Median : 0.00 Median : 0.0000
Mean : 0.5317 Mean :0.05753 Mean : 0.94 Mean : 0.2702
3rd Qu.: 0.0000 3rd Qu.:0.00000 3rd Qu.: 1.00 3rd Qu.: 0.0000
Max. :82.0000 Max. :8.00000 Max. :62.00 Max. :30.0000
V101 V102 V103 V104
Min. : 0.00000 Min. : 0.0000 Min. : 0.0000 Min. : 0.0000
1st Qu.: 0.00000 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.0000
Median : 0.00000 Median : 0.0000 Median : 0.0000 Median : 0.0000
Mean : 0.09132 Mean : 0.2465 Mean : 0.1451 Mean : 0.0815
3rd Qu.: 0.00000 3rd Qu.: 0.0000 3rd Qu.: 0.0000 3rd Qu.: 0.0000
Max. :18.00000 Max. :68.0000 Max. :29.0000 Max. :59.0000
V105 V106 V107 V108
Min. : 0.0000 Min. : 0.000 Min. :1 Min. :0.000
1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.:1 1st Qu.:1.000
Median : 0.0000 Median : 0.000 Median :1 Median :1.000
Mean : 0.1884 Mean : 0.115 Mean :1 Mean :1.007
3rd Qu.: 0.0000 3rd Qu.: 0.000 3rd Qu.:1 3rd Qu.:1.000
Max. :99.0000 Max. :80.000 Max. :1 Max. :8.000
V109 V110 V111 V112
Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
Median :1.000 Median :1.000 Median :1.000 Median :1.000
Mean :1.021 Mean :1.011 Mean :1.004 Mean :1.008
3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
Max. :8.000 Max. :8.000 Max. :8.000 Max. :8.000
V113 V114 V115 V116
Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
Median :1.000 Median :1.000 Median :1.000 Median :1.000
Mean :1.005 Mean :1.014 Mean :1.043 Mean :1.021
3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
Max. :8.000 Max. :9.000 Max. :9.000 Max. :9.000
V117 V118 V119 V120
Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
Median :1.000 Median :1.000 Median :1.000 Median :1.000
Mean :1.001 Mean :1.002 Mean :1.001 Mean :1.002
3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
Max. :2.000 Max. :3.000 Max. :2.000 Max. :4.000
V121 V122 V123 V124
Min. :0.000 Min. :0.000 Min. : 0.000 Min. : 0.000
1st Qu.:1.000 1st Qu.:1.000 1st Qu.: 1.000 1st Qu.: 1.000
Median :1.000 Median :1.000 Median : 1.000 Median : 1.000
Mean :1.006 Mean :1.003 Mean : 1.038 Mean : 1.106
3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.: 1.000 3rd Qu.: 1.000
Max. :4.000 Max. :4.000 Max. :12.000 Max. :13.000
V125 V126 V127 V128
Min. : 0.000 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 1.000 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 1.000 Median : 0.0 Median : 0.0 Median : 0.0
Mean : 1.056 Mean : 65.6 Mean : 205.8 Mean : 106.4
3rd Qu.: 1.000 3rd Qu.: 0.0 3rd Qu.: 115.9 3rd Qu.: 0.0
Max. :12.000 Max. :519038.5 Max. :544500.0 Max. :519038.5
V129 V130 V131
Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00
Median : 0.00 Median : 0.00 Median : 0.00
Mean : 10.66 Mean : 107.37 Mean : 35.44
3rd Qu.: 0.00 3rd Qu.: 65.15 3rd Qu.: 0.00
Max. :64800.00 Max. :167200.00 Max. :167200.00
V132 V133 V134
Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0
Mean : 30.3 Mean : 56.9 Mean : 39.3
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :519038.5 Max. :519038.5 Max. :519038.5
V135 V136 V137
Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00
Median : 0.00 Median : 0.00 Median : 0.00
Mean : 24.23 Mean : 41.05 Mean : 31.13
3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.: 0.00
Max. :302500.00 Max. :302500.00 Max. :302500.00
V138 V139 V140 V141
Min. : 0 Min. : 0 Min. : 0.0 Min. :0
1st Qu.: 0 1st Qu.: 0 1st Qu.: 0.0 1st Qu.:0
Median : 0 Median : 1 Median : 1.0 Median :0
Mean : 0 Mean : 1 Mean : 1.1 Mean :0
3rd Qu.: 0 3rd Qu.: 1 3rd Qu.: 1.0 3rd Qu.:0
Max. :20 Max. :28 Max. :59.0 Max. :6
NA's :430906 NA's :430906 NA's :430906 NA's :430906
V142 V143 V144 V145
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
Mean : 0.1 Mean : 1.8 Mean : 2.8 Mean : 17.1
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :11.0 Max. :56.0 Max. :85.0 Max. :279.0
NA's :430906 NA's :430636 NA's :430636 NA's :430636
V146 V147 V148 V149
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 1.0 Median : 1.0
Mean : 0.1 Mean : 0.2 Mean : 0.8 Mean : 0.8
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 1.0 3rd Qu.: 1.0
Max. :12.0 Max. :22.0 Max. :11.0 Max. :11.0
NA's :430906 NA's :430906 NA's :430906 NA's :430906
V150 V151 V152 V153
Min. : 1.0 Min. : 1.0 Min. : 1.0 Min. :0.0
1st Qu.: 1.0 1st Qu.: 1.0 1st Qu.: 1.0 1st Qu.:0.0
Median : 1.0 Median : 1.0 Median : 1.0 Median :1.0
Mean : 135.1 Mean : 5.4 Mean : 8.3 Mean :0.7
3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.:1.0
Max. :2277.0 Max. :58.0 Max. :67.0 Max. :8.0
NA's :430636 NA's :430636 NA's :430636 NA's :430906
V154 V155 V156 V157
Min. :0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.:0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median :1.0 Median : 1.0 Median : 1.0 Median : 1.0
Mean :0.8 Mean : 0.8 Mean : 0.8 Mean : 0.8
3rd Qu.:1.0 3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 1.0
Max. :9.0 Max. :11.0 Max. :11.0 Max. :13.0
NA's :430906 NA's :430906 NA's :430906 NA's :430906
V158 V159 V160 V161
Min. : 0.0 Min. : 0 Min. : 0 Min. : 0
1st Qu.: 0.0 1st Qu.: 0 1st Qu.: 0 1st Qu.: 0
Median : 1.0 Median : 0 Median : 0 Median : 0
Mean : 0.8 Mean : 6209 Mean : 98066 Mean : 5
3rd Qu.: 1.0 3rd Qu.: 0 3rd Qu.: 0 3rd Qu.: 0
Max. :13.0 Max. :284130 Max. :3867868 Max. :2000
NA's :430906 NA's :430636 NA's :430636 NA's :430906
V162 V163 V164 V165
Min. : 0.0 Min. : 0.0 Min. : 0 Min. : 0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0 1st Qu.: 0
Median : 0.0 Median : 0.0 Median : 0 Median : 0
Mean : 8.3 Mean : 6.1 Mean : 1038 Mean : 5445
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0 3rd Qu.: 0
Max. :4000.0 Max. :4000.0 Max. :928882 Max. :928882
NA's :430906 NA's :430906 NA's :430636 NA's :430636
V166 V167 V168 V169
Min. : 0 Min. : 0 Min. : 0.0 Min. : 0.0
1st Qu.: 0 1st Qu.: 0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0 Median : 0 Median : 0.0 Median : 0.0
Mean : 1388 Mean : 1 Mean : 1.4 Mean : 0.2
3rd Qu.: 0 3rd Qu.: 0 3rd Qu.: 1.0 3rd Qu.: 0.0
Max. :453750 Max. :238 Max. :257.0 Max. :39.0
NA's :430636 NA's :369957 NA's :369957 NA's :370316
V170 V171 V172 V173
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. :0.0
1st Qu.: 1.0 1st Qu.: 1.0 1st Qu.: 0.0 1st Qu.:0.0
Median : 1.0 Median : 1.0 Median : 0.0 Median :0.0
Mean : 1.5 Mean : 1.7 Mean : 0.1 Mean :0.1
3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 0.0 3rd Qu.:0.0
Max. :63.0 Max. :68.0 Max. :37.0 Max. :9.0
NA's :370316 NA's :370316 NA's :369957 NA's :369957
V174 V175 V176 V177
Min. :0.0 Min. : 0.0 Min. : 1.0 Min. : 0.0
1st Qu.:0.0 1st Qu.: 0.0 1st Qu.: 1.0 1st Qu.: 0.0
Median :0.0 Median : 0.0 Median : 1.0 Median : 0.0
Mean :0.1 Mean : 0.2 Mean : 1.6 Mean : 0.6
3rd Qu.:0.0 3rd Qu.: 0.0 3rd Qu.: 1.0 3rd Qu.: 0.0
Max. :6.0 Max. :22.0 Max. :239.0 Max. :238.0
NA's :370316 NA's :370316 NA's :369957 NA's :369957
V178 V179 V180 V181
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
Mean : 1.1 Mean : 0.8 Mean : 0.5 Mean : 0.2
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :257.0 Max. :257.0 Max. :179.0 Max. :85.0
NA's :369957 NA's :369957 NA's :370316 NA's :369957
V182 V183 V184 V185
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
Mean : 0.4 Mean : 0.3 Mean : 0.1 Mean : 0.2
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :125.0 Max. :106.0 Max. :12.0 Max. :12.0
NA's :369957 NA's :369957 NA's :370316 NA's :370316
V186 V187 V188 V189
Min. : 0.0 Min. : 0.0 Min. : 0 Min. : 0.0
1st Qu.: 1.0 1st Qu.: 1.0 1st Qu.: 1 1st Qu.: 1.0
Median : 1.0 Median : 1.0 Median : 1 Median : 1.0
Mean : 1.1 Mean : 1.5 Mean : 1 Mean : 1.1
3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 1 3rd Qu.: 1.0
Max. :39.0 Max. :158.0 Max. :44 Max. :44.0
NA's :369957 NA's :369957 NA's :370316 NA's :370316
V190 V191 V192 V193
Min. : 0.0 Min. : 0 Min. : 0.0 Min. : 0.0
1st Qu.: 1.0 1st Qu.: 1 1st Qu.: 1.0 1st Qu.: 1.0
Median : 1.0 Median : 1 Median : 1.0 Median : 1.0
Mean : 1.4 Mean : 1 Mean : 1.1 Mean : 1.1
3rd Qu.: 1.0 3rd Qu.: 1 3rd Qu.: 1.0 3rd Qu.: 1.0
Max. :224.0 Max. :20 Max. :37.0 Max. :32.0
NA's :369957 NA's :369957 NA's :369957 NA's :369957
V194 V195 V196 V197
Min. :0.0 Min. : 0 Min. : 0.0 Min. : 0.0
1st Qu.:1.0 1st Qu.: 1 1st Qu.: 1.0 1st Qu.: 1.0
Median :1.0 Median : 1 Median : 1.0 Median : 1.0
Mean :0.9 Mean : 1 Mean : 1.1 Mean : 0.9
3rd Qu.:1.0 3rd Qu.: 1 3rd Qu.: 1.0 3rd Qu.: 1.0
Max. :7.0 Max. :18 Max. :41.0 Max. :11.0
NA's :370316 NA's :370316 NA's :369957 NA's :370316
V198 V199 V200 V201
Min. : 0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 1 1st Qu.: 1.0 1st Qu.: 1.0 1st Qu.: 1.0
Median : 1 Median : 1.0 Median : 1.0 Median : 1.0
Mean : 1 Mean : 1.5 Mean : 1.1 Mean : 1.2
3rd Qu.: 1 3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 1.0
Max. :29 Max. :224.0 Max. :47.0 Max. :47.0
NA's :370316 NA's :369957 NA's :370316 NA's :370316
V202 V203 V204
Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0
Mean : 227.7 Mean : 456.6 Mean : 315.3
3rd Qu.: 0.0 3rd Qu.: 31.4 3rd Qu.: 19.6
Max. :1065496.5 Max. :1065496.5 Max. :1065496.5
NA's :369957 NA's :369957 NA's :369957
V205 V206 V207 V208
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0
Median : 0.0 Median : 0.0 Median : 0.0 Median : 0
Mean : 21.5 Mean : 9.2 Mean : 46.9 Mean : 9
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0
Max. :64800.0 Max. :64800.0 Max. :167200.0 Max. :4000
NA's :369957 NA's :369957 NA's :369957 NA's :370316
V209 V210 V211 V212
Min. : 0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0 Median : 0.0 Median : 0.0 Median : 0.0
Mean : 28 Mean : 14.9 Mean : 138.3 Mean : 237.5
3rd Qu.: 0 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :10000 Max. :4000.0 Max. :928882.0 Max. :958320.0
NA's :370316 NA's :370316 NA's :369957 NA's :369957
V213 V214 V215
Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0
Mean : 165.1 Mean : 63.9 Mean : 137.7
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :928882.0 Max. :453750.0 Max. :756250.0
NA's :369957 NA's :369957 NA's :369957
V216 V217 V218 V219
Min. : 0.0 Min. : 0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0 Median : 0.0 Median : 0.0
Mean : 98.2 Mean : 1 Mean : 1.6 Mean : 1.3
3rd Qu.: 0.0 3rd Qu.: 1 3rd Qu.: 1.0 3rd Qu.: 1.0
Max. :756250.0 Max. :238 Max. :290.0 Max. :290.0
NA's :369957 NA's :379963 NA's :379963 NA's :379963
V220 V221 V222 V223
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 1.0 Median : 1.0 Median : 0.0
Mean : 0.2 Mean : 1.3 Mean : 1.8 Mean : 0.1
3rd Qu.: 0.0 3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 0.0
Max. :47.0 Max. :140.0 Max. :291.0 Max. :11.0
NA's :369375 NA's :369375 NA's :369375 NA's :379963
V224 V225 V226 V227
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
Mean : 0.3 Mean : 0.2 Mean : 0.3 Mean : 0.1
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :91.0 Max. :58.0 Max. :870.0 Max. :131.0
NA's :379963 NA's :379963 NA's :379963 NA's :369375
V228 V229 V230 V231
Min. : 1.0 Min. : 1.0 Min. : 1.0 Min. : 0.0
1st Qu.: 1.0 1st Qu.: 1.0 1st Qu.: 1.0 1st Qu.: 0.0
Median : 1.0 Median : 1.0 Median : 1.0 Median : 0.0
Mean : 1.6 Mean : 2.1 Mean : 1.9 Mean : 0.7
3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 0.0
Max. :239.0 Max. :262.0 Max. :262.0 Max. :238.0
NA's :379963 NA's :379963 NA's :379963 NA's :379963
V232 V233 V234 V235
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
Mean : 1.1 Mean : 0.9 Mean : 3.1 Mean : 0.2
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :290.0 Max. :290.0 Max. :322.0 Max. :18.0
NA's :379963 NA's :379963 NA's :369375 NA's :379963
V236 V237 V238 V239
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
Mean : 0.2 Mean : 0.2 Mean : 0.1 Mean : 0.1
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :57.0 Max. :31.0 Max. :12.0 Max. :12.0
NA's :379963 NA's :379963 NA's :369375 NA's :369375
V240 V241 V242 V243
Min. :1 Min. :1 Min. : 0.0 Min. : 0.0
1st Qu.:1 1st Qu.:1 1st Qu.: 1.0 1st Qu.: 1.0
Median :1 Median :1 Median : 1.0 Median : 1.0
Mean :1 Mean :1 Mean : 1.1 Mean : 1.2
3rd Qu.:1 3rd Qu.:1 3rd Qu.: 1.0 3rd Qu.: 1.0
Max. :6 Max. :3 Max. :27.0 Max. :54.0
NA's :379963 NA's :379963 NA's :379963 NA's :379963
V244 V245 V246 V247
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0
1st Qu.: 1.0 1st Qu.: 0.0 1st Qu.: 1.0 1st Qu.: 1
Median : 1.0 Median : 1.0 Median : 1.0 Median : 1
Mean : 1.1 Mean : 0.8 Mean : 1.4 Mean : 1
3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 1
Max. :33.0 Max. :124.0 Max. :224.0 Max. :11
NA's :379963 NA's :369375 NA's :379963 NA's :379963
V248 V249 V250 V251
Min. : 0.0 Min. : 0 Min. :0.0 Min. : 0.0
1st Qu.: 1.0 1st Qu.: 1 1st Qu.:0.0 1st Qu.: 0.0
Median : 1.0 Median : 1 Median :1.0 Median : 1.0
Mean : 1.1 Mean : 1 Mean :0.8 Mean : 0.8
3rd Qu.: 1.0 3rd Qu.: 1 3rd Qu.:1.0 3rd Qu.: 1.0
Max. :29.0 Max. :19 Max. :8.0 Max. :12.0
NA's :379963 NA's :379963 NA's :369375 NA's :369375
V252 V253 V254 V255
Min. : 0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 1 1st Qu.: 1.0 1st Qu.: 1.0 1st Qu.: 0.0
Median : 1 Median : 1.0 Median : 1.0 Median : 1.0
Mean : 1 Mean : 1.1 Mean : 1.1 Mean : 0.8
3rd Qu.: 1 3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 1.0
Max. :21 Max. :96.0 Max. :39.0 Max. :53.0
NA's :379963 NA's :379963 NA's :379963 NA's :369375
V256 V257 V258 V259
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 1.0 1st Qu.: 1.0 1st Qu.: 0.0
Median : 1.0 Median : 1.0 Median : 1.0 Median : 1.0
Mean : 0.8 Mean : 1.5 Mean : 1.7 Mean : 0.9
3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 1.0 3rd Qu.: 1.0
Max. :53.0 Max. :224.0 Max. :269.0 Max. :131.0
NA's :369375 NA's :379963 NA's :379963 NA's :369375
V260 V261 V262 V263
Min. : 0 Min. : 0.0 Min. : 0 Min. : 0.0
1st Qu.: 1 1st Qu.: 1.0 1st Qu.: 1 1st Qu.: 0.0
Median : 1 Median : 1.0 Median : 1 Median : 0.0
Mean : 1 Mean : 1.1 Mean : 1 Mean : 263.1
3rd Qu.: 1 3rd Qu.: 1.0 3rd Qu.: 1 3rd Qu.: 8.6
Max. :14 Max. :89.0 Max. :34 Max. :1065496.5
NA's :379963 NA's :379963 NA's :379963 NA's :379963
V264 V265 V266
Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0
Mean : 390.7 Mean : 327.9 Mean : 18.5
3rd Qu.: 35.0 3rd Qu.: 22.1 3rd Qu.: 0.0
Max. :1065496.5 Max. :1065496.5 Max. :64800.0
NA's :379963 NA's :379963 NA's :379963
V267 V268 V269 V270
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
Mean : 37.7 Mean : 25.5 Mean : 14.8 Mean : 7.2
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :64800.0 Max. :64800.0 Max. :64800.0 Max. :10791.6
NA's :379963 NA's :379963 NA's :379963 NA's :369375
V271 V272 V273 V274
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
Mean : 12.7 Mean : 8.1 Mean : 169.9 Mean : 231.6
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :10791.6 Max. :10791.6 Max. :928882.0 Max. :928882.0
NA's :369375 NA's :369375 NA's :379963 NA's :379963
V275 V276 V277
Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0
Mean : 197.5 Mean : 69.7 Mean : 115.5
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :928882.0 Max. :453750.0 Max. :756250.0
NA's :379963 NA's :379963 NA's :379963
V278 V279 V280 V281
Min. : 0.0 Min. : 0.0000 Min. : 0.0000 Min. : 0.000
1st Qu.: 0.0 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.000
Median : 0.0 Median : 0.0000 Median : 0.0000 Median : 0.000
Mean : 99.5 Mean : 0.3386 Mean : 0.7443 Mean : 0.092
3rd Qu.: 0.0 3rd Qu.: 0.0000 3rd Qu.: 1.0000 3rd Qu.: 0.000
Max. :756250.0 Max. :86.0000 Max. :108.0000 Max. :30.000
NA's :379963 NA's :3 NA's :3 NA's :6031
V282 V283 V284 V285
Min. : 0.000 Min. : 0.000 Min. :0.0000 Min. : 0.000
1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.: 0.000
Median : 1.000 Median : 1.000 Median :0.0000 Median : 0.000
Mean : 0.825 Mean : 1.008 Mean :0.0871 Mean : 1.151
3rd Qu.: 1.000 3rd Qu.: 1.000 3rd Qu.:0.0000 3rd Qu.: 1.000
Max. :63.000 Max. :68.000 Max. :8.0000 Max. :90.000
NA's :6031 NA's :6031 NA's :3 NA's :3
V286 V287 V288 V289
Min. :0.00000 Min. : 0.0000 Min. :0.000 Min. : 0.000
1st Qu.:0.00000 1st Qu.: 0.0000 1st Qu.:0.000 1st Qu.: 0.000
Median :0.00000 Median : 0.0000 Median :0.000 Median : 0.000
Mean :0.03063 Mean : 0.3538 Mean :0.187 Mean : 0.241
3rd Qu.:0.00000 3rd Qu.: 0.0000 3rd Qu.:0.000 3rd Qu.: 0.000
Max. :6.00000 Max. :31.0000 Max. :6.000 Max. :11.000
NA's :3 NA's :3 NA's :6031 NA's :6031
V290 V291 V292 V293
Min. : 1.00 Min. : 1.000 Min. : 1.000 Min. : 0.0000
1st Qu.: 1.00 1st Qu.: 1.000 1st Qu.: 1.000 1st Qu.: 0.0000
Median : 1.00 Median : 1.000 Median : 1.000 Median : 0.0000
Mean : 1.12 Mean : 1.405 Mean : 1.212 Mean : 0.1569
3rd Qu.: 1.00 3rd Qu.: 1.000 3rd Qu.: 1.000 3rd Qu.: 0.0000
Max. :49.00 Max. :250.000 Max. :75.000 Max. :62.0000
NA's :3 NA's :3 NA's :3 NA's :3
V294 V295 V296 V297
Min. : 0.0000 Min. : 0.0000 Min. : 0.000 Min. : 0.00000
1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.00000
Median : 0.0000 Median : 0.0000 Median : 0.000 Median : 0.00000
Mean : 0.4561 Mean : 0.2586 Mean : 0.242 Mean : 0.09062
3rd Qu.: 0.0000 3rd Qu.: 0.0000 3rd Qu.: 0.000 3rd Qu.: 0.00000
Max. :246.0000 Max. :78.0000 Max. :179.000 Max. :85.00000
NA's :3 NA's :3 NA's :6031 NA's :3
V298 V299 V300 V301
Min. : 0.0000 Min. : 0.0000 Min. : 0.000 Min. : 0.000
1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000
Median : 0.0000 Median : 0.0000 Median : 0.000 Median : 0.000
Mean : 0.2016 Mean : 0.1274 Mean : 0.057 Mean : 0.066
3rd Qu.: 0.0000 3rd Qu.: 0.0000 3rd Qu.: 0.000 3rd Qu.: 0.000
Max. :125.0000 Max. :106.0000 Max. :14.000 Max. :14.000
NA's :3 NA's :3 NA's :6031 NA's :6031
V302 V303 V304 V305
Min. : 0.0000 Min. : 0.0000 Min. : 0.0000 Min. :1
1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.:1
Median : 0.0000 Median : 0.0000 Median : 0.0000 Median :1
Mean : 0.2868 Mean : 0.3218 Mean : 0.3015 Mean :1
3rd Qu.: 1.0000 3rd Qu.: 1.0000 3rd Qu.: 1.0000 3rd Qu.:1
Max. :13.0000 Max. :17.0000 Max. :17.0000 Max. :2
NA's :3 NA's :3 NA's :3 NA's :3
V306 V307 V308
Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0
Mean : 83.8 Mean : 259.4 Mean : 136.9
3rd Qu.: 0.0 3rd Qu.: 156.0 3rd Qu.: 41.3
Max. :718740.0 Max. :958320.0 Max. :718740.0
NA's :3 NA's :3 NA's :3
V309 V310 V311
Min. : 0.00 Min. : 0.0 Min. : 0.00
1st Qu.: 0.00 1st Qu.: 0.0 1st Qu.: 0.00
Median : 0.00 Median : 0.0 Median : 0.00
Mean : 12.84 Mean : 127.2 Mean : 5.15
3rd Qu.: 0.00 3rd Qu.: 106.0 3rd Qu.: 0.00
Max. :64800.00 Max. :167200.0 Max. :64800.00
NA's :3 NA's :3 NA's :3
V312 V313 V314 V315
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00
Median : 0.00 Median : 0.00 Median : 0.00 Median : 0.00
Mean : 42.73 Mean : 21.24 Mean : 43.27 Mean : 26.73
3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.: 0.00
Max. :167200.00 Max. :4727.96 Max. :7539.75 Max. :4727.96
NA's :3 NA's :6031 NA's :6031 NA's :6031
V316 V317 V318
Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0
Mean : 42.1 Mean : 83.8 Mean : 56.1
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :718740.0 Max. :958320.0 Max. :718740.0
NA's :3 NA's :3 NA's :3
V319 V320 V321 V322
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
Mean : 27.6 Mean : 46.8 Mean : 36.8 Mean : 0.4
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :453750.0 Max. :605000.0 Max. :605000.0 Max. :86.0
NA's :3 NA's :3 NA's :3 NA's :430260
V323 V324 V325 V326
Min. : 0.0 Min. : 0.0 Min. :0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.:0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median :0 Median : 0.0
Mean : 1.2 Mean : 0.7 Mean :0 Mean : 0.5
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.:0 3rd Qu.: 0.0
Max. :128.0 Max. :108.0 Max. :8 Max. :44.0
NA's :430260 NA's :430260 NA's :430260 NA's :430260
V327 V328 V329 V330
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
Mean : 0.2 Mean : 0.2 Mean : 0.5 Mean : 0.3
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :31.0 Max. :85.0 Max. :125.0 Max. :106.0
NA's :430260 NA's :430260 NA's :430260 NA's :430260
V331 V332 V333
Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0
Mean : 315.1 Mean : 440.5 Mean : 381.1
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :1040657.5 Max. :1040657.5 Max. :1040657.5
NA's :430260 NA's :430260 NA's :430260
V334 V335 V336 V337
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0 Median : 0.0 Median : 0.0
Mean : 24.7 Mean : 58.3 Mean : 35.5 Mean : 99.1
3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :64800.0 Max. :64800.0 Max. :64800.0 Max. :375000.0
NA's :430260 NA's :430260 NA's :430260 NA's :430260
V338 V339
Min. : 0.0 Min. : 0.0
1st Qu.: 0.0 1st Qu.: 0.0
Median : 0.0 Median : 0.0
Mean : 155.6 Mean : 139.8
3rd Qu.: 0.0 3rd Qu.: 0.0
Max. :612500.0 Max. :612500.0
NA's :430260 NA's :430260 2. Explore
(1) identityD: train_i + test_i
identityD <- rbind(data.frame(train_i,datatype='train'),data.frame(test_i,datatype='test'))
str(identityD) # 286140 x 42'data.frame': 286140 obs. of 42 variables:
$ TransactionID: int 2987004 2987008 2987010 2987011 2987016 2987017 2987022 2987038 2987040 2987048 ...
$ id_01 : num 0 -5 -5 -5 0 -5 -15 0 -10 -5 ...
$ id_02 : num 70787 98945 191631 221832 7460 ...
$ id_03 : num NA NA 0 NA 0 3 NA 0 0 NA ...
$ id_04 : num NA NA 0 NA 0 0 NA 0 0 NA ...
$ id_05 : num NA 0 0 0 1 3 NA 0 0 0 ...
$ id_06 : num NA -5 0 -6 0 0 NA -10 0 0 ...
$ id_07 : num NA NA NA NA NA NA NA NA NA NA ...
$ id_08 : num NA NA NA NA NA NA NA NA NA NA ...
$ id_09 : num NA NA 0 NA 0 3 NA 0 0 NA ...
$ id_10 : num NA NA 0 NA 0 0 NA 0 0 NA ...
$ id_11 : num 100 100 100 100 100 100 NA 100 100 100 ...
$ id_12 : Factor w/ 2 levels "Found","NotFound": 2 2 2 2 2 2 2 1 2 2 ...
$ id_13 : num NA 49 52 52 NA 52 14 NA 52 52 ...
$ id_14 : num -480 -300 NA NA -300 -300 NA -300 NA NA ...
$ id_15 : Factor w/ 3 levels "Found","New",..: 2 2 1 2 1 1 NA 1 1 2 ...
$ id_16 : Factor w/ 2 levels "Found","NotFound": 2 2 1 2 1 1 NA 1 1 2 ...
$ id_17 : num 166 166 121 225 166 166 NA 166 121 225 ...
$ id_18 : num NA NA NA NA 15 18 NA 15 NA NA ...
$ id_19 : num 542 621 410 176 529 529 NA 352 410 484 ...
$ id_20 : num 144 500 142 507 575 600 NA 533 142 507 ...
$ id_21 : num NA NA NA NA NA NA NA NA NA NA ...
$ id_22 : num NA NA NA NA NA NA NA NA NA NA ...
$ id_23 : Factor w/ 3 levels "IP_PROXY:ANONYMOUS",..: NA NA NA NA NA NA NA NA NA NA ...
$ id_24 : num NA NA NA NA NA NA NA NA NA NA ...
$ id_25 : num NA NA NA NA NA NA NA NA NA NA ...
$ id_26 : num NA NA NA NA NA NA NA NA NA NA ...
$ id_27 : Factor w/ 2 levels "Found","NotFound": NA NA NA NA NA NA NA NA NA NA ...
$ id_28 : Factor w/ 2 levels "Found","New": 2 2 1 2 1 1 NA 1 1 2 ...
$ id_29 : Factor w/ 2 levels "Found","NotFound": 2 2 1 2 1 1 NA 1 1 2 ...
$ id_30 : Factor w/ 87 levels "Android","Android 4.4.2",..: 8 28 NA NA 45 70 NA 1 NA NA ...
$ id_31 : Factor w/ 172 levels "android","android browser 4.0",..: 120 93 32 32 32 32 NA 32 32 32 ...
$ id_32 : num 32 32 NA NA 24 24 NA 32 NA NA ...
$ id_33 : Factor w/ 461 levels "0x0","1023x767",..: 165 49 NA NA 41 61 NA 133 NA NA ...
$ id_34 : Factor w/ 4 levels "match_status:-1",..: 4 3 NA NA 4 4 NA 4 NA NA ...
$ id_35 : logi TRUE TRUE FALSE FALSE TRUE TRUE ...
$ id_36 : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ id_37 : logi TRUE FALSE TRUE TRUE TRUE TRUE ...
$ id_38 : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
$ DeviceType : Factor w/ 2 levels "desktop","mobile": 2 2 1 1 1 1 NA 2 1 1 ...
$ DeviceInfo : Factor w/ 2799 levels "0PAJ5","0PJA2",..: 1009 469 1663 NA 723 1663 NA NA 1663 1663 ...
$ datatype : Factor w/ 2 levels "train","test": 1 1 1 1 1 1 1 1 1 1 ...변수별 보기
(1)-1. Categorical Features
TransactionID
summary(identityD$TransactionID) Min. 1st Qu. Median Mean 3rd Qu. Max.
2987004 3195766 3571072 3601257 3999726 4170239 identityD %>% group_by(datatype) %>% summarise(n=n(),min=min(TransactionID),max=max(TransactionID))# A tibble: 2 x 4
datatype n min max
<fct> <int> <dbl> <dbl>
1 train 144233 2987004 3577534
2 test 141907 3663586 4170239- 7글자…
- id가 연속적이지 X
- train 보다 test 가 더 뒤의 id
- 중복된 값 X
- 모델링에는 X
DeviceType
summary(identityD$DeviceType)desktop mobile NA's
159568 118173 8399 - NA 처리 필요 -> 일단 missing으로 대체
identityD$DeviceType <- as.character(identityD$DeviceType)
identityD$DeviceType <- as.factor(ifelse(is.na(identityD$DeviceType),'missing',identityD$DeviceType))
summary(identityD$DeviceType)desktop missing mobile
159568 8399 118173 - train, test 데이터 별 NA를 보면
with(identityD,table(DeviceType,datatype)) datatype
DeviceType train test
desktop 85165 74403
missing 3423 4976
mobile 55645 62528round(with(identityD,prop.table(table(DeviceType,datatype))),2) datatype
DeviceType train test
desktop 0.30 0.26
missing 0.01 0.02
mobile 0.19 0.22- 일단, 전체 데이터에서 NA가 3%로 밖에 안되고
- train, test 데이터에 고르게 분포
DeviceInfo
str(identityD$DeviceInfo) Factor w/ 2799 levels "0PAJ5","0PJA2",..: 1009 469 1663 NA 723 1663 NA NA 1663 1663 ...- level이 무려 2799개
round(NROW(which(is.na(identityD$DeviceInfo)))/NROW(identityD),2)[1] 0.18- NA 비율도 무려 전체의 18%
level이 너무 많으므로 합치기
tmp <- identityD %>% group_by(DeviceInfo) %>%
summarise(n=n(),prop=round(n/NROW(identityD),3)) %>% arrange(desc(prop))
tmp# A tibble: 2,800 x 3
DeviceInfo n prop
<fct> <int> <dbl>
1 Windows 92710 0.324
2 <NA> 52417 0.183
3 iOS Device 38502 0.135
4 MacOS 23722 0.083
5 Trident/7.0 12330 0.043
6 rv:11.0 2650 0.009
7 rv:57.0 968 0.003
8 SM-G532M Build/MMB29T 980 0.003
9 SM-G610M Build/MMB29K 804 0.003
10 SM-J700M Build/MMB29K 829 0.003
# … with 2,790 more rows- 그냥 일단 ‘Windows’, NA, ‘iOS Device’, ‘MacOS’, ’etc’로 5개로
identityD$DeviceInfo <- as.character(identityD$DeviceInfo)
identityD$DeviceInfo <- as.factor(ifelse(is.na(identityD$DeviceInfo),NA,
ifelse(!(identityD$DeviceInfo %in% c('Windows','iOS Device','MacOS')),
'etc',identityD$DeviceInfo)))
summary(identityD$DeviceInfo) etc iOS Device MacOS Windows NA's
78789 38502 23722 92710 52417 round(with(identityD,prop.table(table(DeviceInfo,datatype))),2) datatype
DeviceInfo train test
etc 0.17 0.17
iOS Device 0.08 0.08
MacOS 0.05 0.05
Windows 0.20 0.19- train, test 데이터에 고르게 분포
id_12
summary(identityD$id_12) Found NotFound
42220 243920 - NA 없음
round(prop.table(table(identityD$id_12,identityD$datatype)),2)
train test
Found 0.07 0.07
NotFound 0.43 0.42- train, test 데이터에 고르게 분포
Found->1, NotFound->0 으로 변환
identityD$id_12 <- ifelse(as.character(identityD$id_12)=='Found',1,0)id_13
summary(identityD$id_13) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
10.00 27.00 52.00 42.41 52.00 64.00 28534 round(NROW(which(is.na(identityD$id_13)))/NROW(identityD),2)[1] 0.1- NA가 10% 정도…
table(identityD$id_13)
10 11 12 13 14 15 16 17 18 19
1 890 2 21 6427 520 3 3 1218 1147
20 21 22 23 24 25 26 27 28 29
3804 8 5 1 531 1302 2 73282 77 1
30 31 32 33 34 35 36 37 38 39
9 38 10 10048 1 317 25 3 22 45
40 41 42 43 44 45 46 47 48 49
1 1086 9 907 87 26 3 3 12 26365
50 51 52 53 54 55 56 57 58 59
1 349 109760 4 67 781 19 3 17 1
60 61 62 63 64
3 108 1752 2050 14429 - 10 ~ 64까지 55개 연속적인 값… 명목척도? 서열척도? 등간척도?
ggplot(identityD,aes(x=id_13, color=datatype)) + geom_histogram(fill='white',position='dodge',bins=100)
- train, test 데이터에 고르게 분포 X
id_14
summary(identityD$id_14) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-720.0 -360.0 -300.0 -344.5 -300.0 720.0 134739 round(NROW(which(is.na(identityD$id_14)))/NROW(identityD),2)[1] 0.47- NA가 무려 47%…
table(identityD$id_14)
-720 -660 -600 -540 -480 -420 -360 -300 -240 -210 -180 -120
1 2 1010 195 24245 8751 31196 83733 297 5 204 2
0 60 120 180 240 270 300 330 360 420 480 540
364 687 98 73 24 20 20 23 3 56 138 118
570 600 660 720
4 113 1 18 - seq(-720,720,60) 에서(25개) 270, 330, 570 이 추가… 60 or 30 단위인거 보면 시간인가… 등간척도? 비율척도?
ggplot(identityD,aes(x=id_14, color=datatype)) + geom_histogram(fill='white',position='dodge',bins=100)
- train, test 데이터에 고르게 분포
id_15
summary(identityD$id_15) Found New Unknown NA's
135690 119397 22875 8178 round(NROW(which(is.na(identityD$id_15)))/NROW(identityD),2)[1] 0.03- NA가 3% 정도…
round(prop.table(table(ifelse(is.na(as.character(identityD$id_15)),'missing',as.character(identityD$id_15)),identityD$datatype)),2)
train test
Found 0.24 0.24
missing 0.01 0.02
New 0.22 0.20
Unknown 0.04 0.04- train, test 데이터에 고르게 분포
id_16
summary(identityD$id_16) Found NotFound NA's
132805 122282 31053 round(NROW(which(is.na(identityD$id_16)))/NROW(identityD),2)[1] 0.11- NA가 11% 정도…
round(prop.table(table(ifelse(is.na(as.character(identityD$id_16)),'missing',as.character(identityD$id_16)),identityD$datatype)),2)
train test
Found 0.23 0.23
missing 0.05 0.06
NotFound 0.22 0.21- train, test 데이터에 고르게 분포
Found->1, NotFound->0 으로 변환
identityD$id_16 <- ifelse(as.character(identityD$id_16)=='Found',1,
ifelse(as.character(identityD$id_16)=='NotFound',0,NA))id_17
summary(identityD$id_17) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
100.0 166.0 166.0 190.3 225.0 229.0 10805 round(NROW(which(is.na(identityD$id_17)))/NROW(identityD),2)[1] 0.04- NA가 4% 정도…
table(identityD$id_17)
100 101 102 103 104 105 106 107 108 109
539 22 1443 1 1 1 168 33 3 8
110 111 112 113 114 115 116 117 118 119
18 35 1 3 24 1 9 74 17 64
120 121 122 123 124 125 126 127 128 129
6 646 10 2 6 2 19 24 10 22
130 131 133 134 135 136 137 138 139 140
21 16 110 72 83 20 84 37 1 2
141 142 143 144 145 146 147 148 149 150
39 285 96 183 2 107 34 482 197 305
151 152 153 154 155 156 157 158 159 160
8 11 5 1 10 14 11 7 690 1
161 162 163 164 165 166 167 168 169 170
13 18 9 13 2 150938 3 52 2 2
171 172 173 174 175 176 177 178 179 180
43 1 1 5 1 4 31 21 2 10
181 182 183 184 185 186 187 188 189 190
1 50 138 52 16 3 1 6 34 16
191 192 193 194 195 196 197 198 199 200
378 185 2 43 103 1 9 5 59 40
201 202 203 204 205 207 208 209 210 211
2 7 21 3 67 6 19 1 96 1
212 213 214 215 216 217 218 219 220 222
81 3 10 8 27 27 140 1 1 3
223 224 225 226 227 228 229
3 1 116313 19 1 14 1 - 100 ~ 229 에서(130개) 132, 206, 221 가 빠진 127개 level… 서열척도? 등간척도? 비율척도?
ggplot(identityD,aes(x=id_17, color=datatype)) + geom_histogram(fill='white',position='dodge',bins=100)
- train, test 데이터에 고르게 분포
id_18
summary(identityD$id_18) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
10.00 13.00 15.00 14.53 15.00 29.00 190152 round(NROW(which(is.na(identityD$id_18)))/NROW(identityD),2)[1] 0.66- NA가 무려 66%…
table(identityD$id_18)
10 11 12 13 14 15 16 17 18 19 20 21
1 368 8765 25223 12 53476 1 4489 1113 1 960 207
23 24 25 26 27 28 29
12 158 3 903 275 7 14 - 10 ~ 29 에서(20개) 22가 빠진 19개 level… 서열척도? 등간척도? 비율척도?
ggplot(identityD,aes(x=id_18, color=datatype)) + geom_histogram(fill='white',position='dodge',bins=100)
- train, test 데이터에 약간 고르게 분포
id_19
summary(identityD$id_19) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
100.0 266.0 337.0 351.6 427.0 671.0 10916 round(NROW(which(is.na(identityD$id_19)))/NROW(identityD),2)[1] 0.04- NA가 4% 정도…
table(identityD$id_19)
100 101 102 103 104 105 106 107 108 109 110 111
9998 19 31 88 43 73 14 77 6 31 30 26
112 113 114 115 116 117 118 119 120 121 122 123
42 33 5 1 7 32 2 27 19 117 2634 17
124 125 126 127 128 129 130 131 132 133 134 135
19 20 18 272 17 17 22 7 31 24 61 1
136 137 138 139 140 141 142 143 144 145 146 147
57 13 15 657 8 2 73 1 1078 169 225 3
148 149 150 151 152 153 154 155 156 157 158 159
2 21 25 7 63 9518 14 34 5 21 2 2
160 161 162 163 164 165 166 167 168 169 170 171
95 211 18 32 11 3 21 26 31 3 27 2
172 173 174 175 176 177 178 179 180 181 182 183
1 18 1 3 7786 151 129 2 19 312 22 7
184 185 186 187 188 189 190 191 192 193 194 195
34 4 226 14 50 8 6 22 24 6333 42 3
196 197 198 199 201 202 203 204 205 206 207 208
43 411 1 26 138 24 24 229 25 1 4 31
209 210 211 212 213 214 215 216 217 218 219 220
45 31 11 3 3 588 9133 2773 6 27 3 20
221 222 223 224 225 226 227 228 229 231 232 233
19 110 275 32 80 27 1 40 18 20 4 3
234 235 236 237 238 239 240 241 242 243 244 245
5 18 45 24 8 28 10 19 220 29 14 19
246 247 248 249 250 251 252 253 254 255 256 257
18 35 17 18 51 34 20 233 2696 4 118 19
258 259 260 261 262 263 265 266 267 268 269 270
42 27 16 101 5 8 18 39541 16 69 61 457
271 272 273 274 275 276 277 278 279 280 281 282
6385 94 2 19 228 2 1059 300 72 139 5 47
283 284 285 286 287 288 289 290 291 292 293 294
30 20 1 4656 18 11 4 7272 4 5 1 25
295 296 297 298 299 300 301 302 303 304 305 306
21 57 24 31 107 29 1 22 394 37 46 47
307 308 309 310 311 312 313 314 315 316 317 318
612 117 8 52 51 7080 5 28 56 4 4005 26
319 320 321 322 323 324 325 326 327 328 329 330
1 50 3509 49 19 2 98 2 30 28 4 20
331 332 333 334 335 336 337 338 339 340 341 342
2 30 38 4 75 18 645 4 624 75 2517 45
343 344 345 346 347 348 349 350 351 352 353 354
29 19 736 24 93 530 19 177 103 7783 4 4
355 356 357 358 359 360 361 362 363 364 365 366
23 25 3 34 42 129 1 27 5 18 7 21
367 368 369 370 371 372 373 374 375 376 377 378
4 98 31 36 31 440 461 86 25 4 22 10
379 380 381 382 383 384 385 386 387 388 389 390
84 358 25 13 122 2375 29 24 22 11 2 3604
391 392 393 394 395 396 397 398 399 400 401 402
57 2 152 36 488 25 481 20 1219 424 42 7
403 404 405 406 407 408 409 410 411 412 413 414
11 170 22 9 78 18 33 22414 66 1 1 584
415 416 417 418 419 420 421 422 423 424 425 426
15 3 9010 56 6 176 95 22 1 122 3 13
427 428 429 430 431 432 433 434 435 436 437 438
16646 94 25 35 53 47 19 8 24 29 26 63
439 440 441 442 443 444 445 446 447 448 449 450
52 101 56 24 9 70 66 26 44 4 219 2
451 452 453 454 455 456 457 458 459 460 461 462
50 63 14 1803 12 20 117 3 30 24 1 5
463 464 465 466 467 468 469 470 471 472 473 474
28 1 7 20 1 39 49 25 20 69 28 103
475 476 477 478 479 480 481 482 483 484 485 486
22 25 26 103 5 20 81 6 20 1751 1 1
487 488 489 490 491 492 493 494 495 496 497 498
277 3 26 61 45 2869 22 52 90 27 113 13
499 500 501 502 503 504 505 506 507 508 509 510
96 67 84 25 7 4 12 20 121 24 56 41
511 512 513 514 515 516 517 518 519 520 521 522
25 4 1 49 5 111 215 2 28 39 18 30
523 524 525 526 527 528 529 530 531 532 533 535
9 1 260 57 50 20 15893 47 18 21 538 20
536 537 538 539 540 541 542 543 544 545 546 547
76 41 23 3 23 13 9784 27 5 36 2 22
548 549 550 551 552 553 554 555 556 557 558 559
4996 28 7 3 2 94 39 22 34 2 4 120
560 561 562 563 564 565 566 567 568 569 570 571
17 26 6 47 54 16 18 4844 2 38 28 1
572 573 574 575 576 577 578 579 580 581 582 583
6 3 1247 38 20 27 39 90 1 36 38 50
584 585 586 587 588 589 590 591 592 593 594 595
1 432 3 28 24 2 68 15 6 28 2 27
596 597 598 599 600 601 602 603 604 605 606 607
22 53 30 18 58 16 47 34 4 2 2 1422
608 609 610 611 612 613 614 615 616 617 618 619
44 80 10 57 6 249 9 83 2 53 29 20
620 621 622 623 624 625 626 627 628 629 630 631
240 4029 322 69 1 45 1 2 15 154 747 4
632 633 634 635 636 637 638 639 640 641 642 643
27 4069 87 10 26 1 51 3 21 20 66 3
644 645 646 647 648 649 650 651 652 653 654 655
109 36 84 8 19 168 367 65 22 151 25 18
656 657 658 659 660 661 662 663 664 665 666 667
4 4 5 21 3 29 2 7 17 51 20 68
668 669 670 671
6 123 53 3 - 100 ~ 671 에서(572개) 200,230,264,534가 빠진 568개 level… 서열척도? 등간척도? 비율척도?
ggplot(identityD,aes(x=id_19, color=datatype)) + geom_histogram(fill='white',position='dodge',bins=100)
- train, test 데이터에 약간 고르게 분포
id_20
summary(identityD$id_20) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
100.0 256.0 472.0 406.4 539.0 661.0 11246 round(NROW(which(is.na(identityD$id_20)))/NROW(identityD),2)[1] 0.04- NA가 4% 정도…
table(identityD$id_20)
100 101 102 103 104 105 106 107 108 109 110 111
1115 2 10 89 15 7 3 1602 1 1 9 270
112 113 114 115 116 117 118 119 120 121 122 123
7 8 1 1 2 10 11 6 2 95 1843 4
124 125 126 127 128 129 130 131 132 133 134 135
11 3 3 4768 2 3 5 2 1 4 31 1
136 137 138 139 140 141 142 143 144 145 146 147
23 2 2 845 2 1 393 1 1312 34 1883 1
148 149 150 151 152 153 154 155 156 157 158 159
1 3 5 2 18 771 4 10 1 4 1 4
160 161 162 163 165 166 167 168 169 170 171 172
226 6434 2 1 8 1 5 10 1 6 1 1
173 174 175 176 177 178 179 180 181 182 183 184
3 1 1 42 2794 1043 1 2 815 4 2 6
185 186 187 188 190 191 192 193 194 195 196 197
1 858 2 17 1 1 5 157 16 1 12 93
198 199 201 202 203 204 205 206 207 208 209 210
1 4 53 4 5 230 7 1 1 4 16 9
211 212 213 214 215 216 217 218 219 220 221 222
2 1 4 10945 427 956 1 6 1 3 3 20843
223 224 225 226 227 228 229 230 231 232 233 234
374 11 2268 3 1 14 2 1 7 1 1 1
235 236 237 238 239 240 241 242 243 244 245 246
2 18 4 2 6 2 2 96 9 5 2 2
247 248 249 250 251 252 253 254 255 256 257 258
14 2 2 19 11 3 574 275 1 3940 2 12
259 260 261 262 263 264 265 266 267 268 269 270
5 3 113 1 2 1 6 2355 2 45 242 22
271 272 273 274 275 276 277 278 279 280 281 282
62 539 1 3 7 1 3097 1656 1 2738 1 17
283 284 285 286 288 289 290 292 293 294 295 296
17 3 1 7 2 1 23 1 1 5 5 53
298 299 300 301 302 303 304 305 306 307 308 309
10 1342 83 1 4 8 8 5034 11 11 12 2
310 311 312 313 314 315 316 317 318 320 321 322
62 37 84 1 420 3582 1 16 4 175 107 8
323 324 325 326 327 328 329 330 331 332 333 334
2 558 15427 1 13 7 1 3 1 20 6979 1
335 336 337 338 339 340 341 342 343 344 345 346
56 2 144 1 20 830 16 36 7 1 17 4
347 348 349 350 351 352 353 354 355 356 357 358
18 16 3 137 8 12 1 1 4 5 1 148
359 360 361 362 363 364 365 366 367 368 369 370
17 724 1 7 1 2 2 3 1 4384 39 34
371 372 373 374 375 376 377 378 379 380 381 382
21 6 16 8 5 1 4 2 41 6 25 2
383 384 385 386 387 388 389 390 391 392 393 394
89 123 15 5 4 2 1 8 3504 1 54 1052
396 397 398 399 400 401 402 403 404 405 406 407
5 22 3 2 66 3111 1 2 558 4 2 6
408 409 410 411 413 414 415 416 417 418 419 420
3 5 6 942 1 21 2 1 1859 5 1 43
421 422 423 424 425 426 427 428 429 430 431 432
1 3 1 377 1 2 34 23 9 20 14 17
433 434 435 436 437 438 439 440 441 442 443 444
3 2 7 22 8 3 426 8 3 6 2 69
445 446 447 448 449 451 452 453 454 455 456 457
6 66 5 1 5 20 5 2 12 2 101 4
458 459 460 461 462 463 464 465 466 467 468 469
2 6 72 1 1 44 1 2 8 1 9 1837
471 472 473 474 475 476 477 478 479 480 481 482
7 1835 16 145 5 63 21 5 1 4 70 1
483 484 485 486 487 488 489 490 491 492 493 494
104 2303 4 1 24 1 4816 3 8 1 8 2
495 496 497 498 499 500 501 502 503 504 505 506
4 17 2315 47 12 5364 389 6 1 1 2 2
507 508 509 510 511 512 513 514 515 516 517 518
34192 18 13 3 27 1 1 425 1 1 3 1
519 520 521 522 523 524 525 526 527 528 529 530
13 5 1776 4 2 1 1 9 11 2 1 25
531 532 533 534 535 536 537 538 539 540 541 542
31 2 12483 1 3705 13 8 3 6 2 579 2
543 544 545 546 547 548 549 550 551 552 553 554
5 1 4 1 2 5 10970 3 1 1 1 1
555 556 557 558 559 560 561 562 563 564 565 566
2 111 1 1 6 4 2704 10 12280 15 1473 3227
567 568 569 570 571 572 573 574 575 576 577 578
21 159 21 5 1 7 1 1 381 1 4 4
579 580 581 582 583 584 585 586 587 588 589 590
3 1 21 3 18 1 1 5 12 603 14 36
591 593 595 596 597 598 599 600 601 602 603 604
2 3 11814 1 4831 31 1 10345 1 22 3 10
605 606 607 609 610 611 612 613 614 615 617 618
1 1 11 35 569 2238 3802 23 1 2 1 1
619 620 621 622 623 624 625 626 627 628 629 630
33 1 3 21 146 1 2 1 1 19 5 3
631 632 633 634 635 636 637 638 639 640 641 642
2 33 254 113 12 18 1 1352 11 6 31 2
643 644 645 646 647 648 649 650 651 652 653 654
1 6 2 1 1 61 3 24 10 1 2 2
655 656 657 658 659 660 661
10 4 12 1 1 20 1 - 100 ~ 661 에서(562개) 164,189,200,287,291,297,319,395,412,450,470,592,594,608,616가(15개) 빠진 547개 level… 서열척도? 등간척도? 비율척도?
ggplot(identityD,aes(x=id_20, color=datatype)) + geom_histogram(fill='white',position='dodge',bins=100)
- train, test 데이터에 약간 고르게 분포
id_21
summary(identityD$id_21) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
100.0 252.0 277.0 437.3 711.0 854.0 275922 round(NROW(which(is.na(identityD$id_21)))/NROW(identityD),2)[1] 0.96- NA가 무려 96%…?????
table(identityD$id_21)
100 101 102 103 104 105 106 107 108 109 110 111 112 113 114
8 21 1 53 1 4 3 3 1 13 1 1 1 1 2
115 117 118 119 120 121 123 124 125 126 128 129 130 131 132
1 2 1 1 1 2 4 1 3 4 1 1 2 14 1
133 134 135 136 137 138 139 140 141 142 143 144 145 146 147
1 1 1 1 4 5 3 1 1 35 1 2 3 1 2
148 149 150 151 152 153 154 155 156 157 158 160 161 162 163
1 6 1 2 1 1 5 3 1 2 1 2 1 4 1
164 166 167 168 169 170 171 172 173 174 176 177 178 179 180
5 27 1 2 7 1 1 1 2 1 1 1 11 1 1
181 182 183 184 185 186 187 188 189 190 191 192 193 194 195
4 1 13 2 4 1 3 4 25 1 8 1 3 1 1
196 197 198 199 200 201 202 203 204 205 206 207 208 209 210
5 3 1 1 1 6 5 1 4 1 1 1 17 1 3
211 212 213 214 215 216 217 218 219 220 221 222 223 224 225
1 6 1 1 4 2 2 1 9 20 1 18 3 6 1
226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
24 1 469 1 1 1 18 1 2 2 1 2 1 1 2
241 243 244 245 246 247 248 249 250 251 252 253 254 255 256
5 7 1 2 1 2 1 75 1 20 3552 6 12 162 33
257 258 259 260 261 262 263 264 265 266 267 268 269 270 272
12 1 1 1 1 49 3 1 1 13 1 3 1 1 18
273 274 275 276 277 278 279 280 281 282 283 284 285 286 287
1 1 1 1 139 1 1 1 2 1 1 1 1 56 7
288 289 290 291 292 293 294 295 296 297 298 299 300 301 302
8 2 19 1 2 1 3 1 3 1 5 3 1 1 1
303 304 305 306 307 308 309 310 312 313 314 315 316 317 318
3 2 1 36 1 2 5 9 2 3 1 1 1 1 1
319 320 321 322 323 324 325 326 327 329 330 331 332 333 334
1 1 2 1 11 1 2 1 23 3 7 3 4 3 2
335 336 337 338 339 340 341 342 343 344 345 346 347 348 349
1 1 1 10 1 3 1 1 1 1 11 4 7 1 1
350 351 352 353 354 355 356 357 358 359 360 361 362 363 364
2 2 1 26 2 3 1 2 2 1 12 1 1 8 2
365 366 367 368 369 370 371 372 373 374 375 376 377 378 379
1 1 7 13 1 1 2 6 2 1 5 3 1 1 2
380 381 382 383 384 385 386 387 388 389 390 391 392 393 394
1 2 1 5 3 4 5 1 2 1 1 2 3 1 2
395 396 397 398 399 400 401 402 403 404 405 406 407 408 409
2 1 2 1 1 2 2 3 1 2 1 3 1 10 67
410 411 412 413 415 416 417 418 419 420 421 423 424 425 426
17 1 1 1 7 2 2 4 1 25 1 1 1 1 1
427 428 429 431 432 433 434 435 436 437 438 439 440 441 442
3 1 3 1 19 19 1 1 8 3 1 3 47 4 4
443 444 445 446 447 448 449 450 451 452 453 454 455 456 457
3 2 5 7 5 2 1 2 1 4 45 1 2 14 11
458 459 460 461 462 463 464 465 466 467 468 469 470 471 472
1 25 3 1 13 1 1 2 2 1 1 2 45 1 2
473 474 475 476 477 478 479 480 481 482 483 484 485 486 487
1 1 1 1 2 1 6 36 7 1 1 6 1 1 9
488 489 490 491 492 493 494 495 496 497 498 499 500 501 502
3 1 1 6 2 7 2 1 1 1 3 3 35 9 1
503 504 505 506 507 508 509 510 511 512 514 515 516 517 518
1 1 4 1 5 2 1 2 7 3 5 21 3 3 1
519 520 521 522 523 524 525 526 527 528 529 530 531 532 533
2 1 3 1 2 1 9 1 1 18 2 4 2 1 5
534 535 536 537 538 539 540 541 542 543 544 545 546 547 548
1 3 3 79 4 1 1 7 1 2 1 1 1 1 1
549 550 551 552 553 554 555 556 557 558 559 560 561 562 563
1 2 1 1 3 14 4 2 1 1 1 1 4 1 2
564 565 566 567 568 569 570 571 572 573 574 575 576 577 578
4 1 3 2 1 1 7 1 1 8 1 5 167 1 2
579 580 581 582 583 584 585 586 587 588 589 590 591 592 593
1 1 5 15 1 1 15 13 3 1 1 3 14 1 2
594 595 596 597 598 599 600 601 602 603 604 605 606 607 608
1 2 114 5 4 1 2 12 2 1 7 1 1 1 1
609 610 611 612 613 614 615 617 618 619 620 621 622 623 624
24 9 1 2 2 1 1 2 4 19 1 1 3 1 1
625 626 627 628 629 630 631 632 633 634 635 636 637 638 639
2 1 1 1 25 1 1 10 1 2 1 1 1 1 1
640 641 642 643 644 645 646 647 648 649 650 651 652 653 654
2 2 1 3 3 1 2 2 1 2 2 3 1 1 1
655 657 658 659 660 661 662 663 664 665 666 667 668 669 670
1 16 3 2 1 1 1 2 3 1 1 7 68 2 1
671 672 673 674 675 676 677 678 679 680 681 682 683 684 685
5 3 1 1 2 1 1 1 1 2 2 2 6 2 1
686 687 688 689 690 691 692 693 694 695 696 697 698 699 700
3 3 5 1 1 15 3 13 10 1 1 1 3 18 1
701 702 703 704 705 706 707 708 709 710 711 712 713 714 715
1 1 2 1 1 1 1 1 1 1 1796 1 5 1 1
716 717 718 719 720 721 722 723 724 725 726 727 728 729 730
7 1 1 17 12 2 1 2 1 1 1 15 28 1 2
731 733 734 735 736 737 738 739 740 741 743 744 745 746 747
2 1 46 1 1 3 1 1 1 1 1 2 1 1 2
748 749 750 751 752 753 754 755 756 757 758 759 760 761 762
1 1 1 1 4 1 2 115 3 38 1 1 4 12 28
763 764 765 766 767 768 769 770 771 772 773 774 775 776 777
4 2 3 3 1 4 1 110 1 1 3 1 3 2 2
778 779 780 781 782 783 785 786 787 788 789 790 791 792 793
1 2 1 1 1 1 1 3 2 1 1 1 1 1 7
794 795 796 797 798 799 800 801 802 803 804 805 806 807 808
1 18 11 1 3 2 2 17 21 1 2 3 4 2 1
809 810 812 813 814 815 816 817 818 819 820 821 822 823 824
1 1 3 1 4 1 4 2 7 43 16 2 6 1 1
825 826 827 828 829 831 832 833 834 835 836 837 838 839 840
1 1 4 1 6 3 1 3 5 9 12 3 1 17 1
841 842 843 844 845 846 847 848 849 850 851 852 853 854
1 1 1 1 4 2 2 93 154 1 2 5 6 23 - 100 ~ 854 에서(755개) 116,122,127,159,165,175,242,271,311,328,414,422,430,513,616,656,732,742,784,811,83가(21개) 빠진 734개 level… 서열척도? 등간척도? 비율척도?
ggplot(identityD,aes(x=id_21, color=datatype)) + geom_histogram(fill='white',position='dodge',bins=100)
- train, test 데이터에 고르게 분포 X
id_22
summary(identityD$id_22) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
10.00 14.00 14.00 15.67 14.00 44.00 275909 round(NROW(which(is.na(identityD$id_22)))/NROW(identityD),2)[1] 0.96- NA가 무려 96%…?????
table(identityD$id_22)
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
1 1 7 3 9487 8 3 9 2 2 4 15 9 1 21
25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
1 4 18 3 1 1 7 3 64 1 7 14 1 2 13
40 41 42 43 44
3 487 3 23 2 - 10 ~ 44(35개) level…(빠진 수 없음) 서열척도? 등간척도? 비율척도?
ggplot(identityD,aes(x=id_22, color=datatype)) + geom_histogram(fill='white',position='dodge',bins=100)
- train, test 데이터에 고르게 분포
id_23
summary(identityD$id_23) IP_PROXY:ANONYMOUS IP_PROXY:HIDDEN IP_PROXY:TRANSPARENT
2010 1018 7203
NA's
275909 round(NROW(which(is.na(identityD$id_23)))/NROW(identityD),2)[1] 0.96- NA가 무려 96%…?????
table(ifelse(is.na(as.character(identityD$id_23)),'missing',as.character(identityD$id_23)),identityD$datatype)
train test
IP_PROXY:ANONYMOUS 1071 939
IP_PROXY:HIDDEN 609 409
IP_PROXY:TRANSPARENT 3489 3714
missing 139064 136845- train, test 데이터에 고르게 분포
id_24
summary(identityD$id_24) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
10.00 11.00 11.00 12.98 15.00 26.00 276653 round(NROW(which(is.na(identityD$id_24)))/NROW(identityD),2)[1] 0.97- NA가 무려 97%…?????
table(identityD$id_24)
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
1 5666 26 1 1 2948 315 9 104 24 4 222 1 1 141
25 26
9 14 - 10 ~ 26(17개) level…(빠진 수 없음) 서열척도? 등간척도? 비율척도?
ggplot(identityD,aes(x=id_24, color=datatype)) + geom_histogram(fill='white',position='dodge',bins=100)
- train, test 데이터에 약간 고르게 분포
id_25
summary(identityD$id_25) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
100.0 321.0 321.0 330.8 360.0 549.0 275969 round(NROW(which(is.na(identityD$id_25)))/NROW(identityD),2)[1] 0.96- NA가 무려 96%…?????
table(identityD$id_25)
100 101 102 103 104 105 106 107 108 109 110 111 112 113 114
10 3 8 4 8 6 10 1 3 2 25 4 2 4 2
115 116 118 119 120 121 122 123 124 125 126 127 128 130 131
5 3 4 4 2 1 19 97 2 5 64 1 3 15 3
132 133 134 136 137 138 139 140 141 142 143 144 145 146 147
2 4 9 2 2 3 1 3 5 9 54 5 4 5 2
148 150 151 152 153 154 155 156 157 158 159 160 161 162 163
8 1 3 7 1 7 8 2 13 18 6 4 16 3 1
164 165 166 167 168 169 170 171 172 173 174 175 176 177 178
3 2 2 11 5 2 29 8 5 3 4 2 3 19 2
179 180 181 182 183 184 185 186 187 188 189 190 191 192 193
3 3 17 2 3 20 2 2 3 2 1 3 11 1 5
194 195 196 197 198 199 200 201 202 203 204 205 206 207 208
15 2 2 5 5 8 1 4 7 14 11 569 4 2 6
209 210 211 212 213 214 215 216 217 218 219 221 222 223 224
1 9 3 2 2 3 3 17 3 6 2 7 4 4 6
225 226 227 228 229 230 231 232 233 234 235 236 237 238 239
10 12 4 3 3 1 10 3 3 2 3 56 5 3 45
240 241 242 243 244 245 246 247 248 249 250 251 252 253 254
3 3 8 4 2 3 3 48 3 3 36 7 4 6 12
255 256 257 258 259 260 261 262 263 264 265 266 267 268 269
2 1 3 26 5 9 1 1 3 27 5 4 3 6 6
270 271 272 273 274 275 276 277 278 279 280 281 282 283 284
37 4 5 3 3 6 6 12 3 2 1 2 1 1 12
285 286 287 288 289 290 291 292 293 294 295 296 297 298 299
2 15 3 3 2 4 2 2 1 4 4 15 5 3 17
300 301 302 303 304 305 306 307 308 309 310 311 312 313 314
7 7 9 4 6 5 1 5 4 2 8 5 2 2 18
315 316 317 318 319 320 321 323 324 325 326 327 328 329 330
5 2 1 4 2 11 5233 2 4 5 2 5 2 2 13
331 332 333 334 335 336 337 338 339 341 342 343 344 345 346
2 6 3 2 2 3 10 1 18 7 1 4 2 18 5
347 348 349 350 351 352 353 354 355 356 357 358 359 360 361
2 5 3 4 5 2 2 2 25 38 2 2 36 17 4
362 363 364 365 366 367 368 369 370 371 372 373 374 375 376
1 1 3 3 4 2 1 3 4 132 6 4 5 6 2
377 378 379 380 381 382 383 384 385 386 387 388 389 390 391
2 2 9 1 20 3 15 4 51 2 3 2 1 6 24
392 393 394 395 396 397 398 400 401 402 403 404 405 406 407
2 3 1 2 3 2 1 6 1 3 8 14 12 4 1
408 409 410 411 412 413 414 415 416 417 418 419 420 421 422
8 5 6 2 3 2 5 9 2 10 3 1 3 2 21
423 424 425 426 427 428 429 430 431 432 433 434 435 436 437
2 7 1 469 3 2 4 12 21 53 5 2 9 3 5
438 439 440 441 442 443 444 445 446 447 449 450 451 452 453
13 6 7 4 188 23 3 3 2 1 10 2 1 10 4
454 455 456 457 458 459 460 461 462 463 464 465 466 467 468
10 21 3 8 12 8 24 3 2 55 2 3 6 2 4
469 470 471 472 473 474 475 477 478 479 480 481 482 483 484
2 3 4 45 1 2 3 4 4 59 4 31 3 5 6
485 486 487 488 489 490 491 492 493 494 495 496 497 498 499
16 43 1 3 6 4 3 10 1 2 3 3 3 10 18
500 501 502 503 504 505 506 507 508 509 510 511 512 513 514
3 151 2 6 3 51 1 4 7 115 4 36 25 1 5
515 516 517 518 519 520 521 522 523 524 525 526 527 528 529
7 35 1 8 11 3 4 8 13 114 4 1 2 6 2
530 531 532 533 534 535 536 537 538 539 540 541 542 543 544
1 1 5 2 2 11 4 8 12 1 4 3 1 1 6
545 546 547 548 549
16 3 1 11 4 - 100 ~ 549(450개) 에서 117,129,135,149,220,322,340,399,448,476(10개) 서열척도? 등간척도? 비율척도?
ggplot(identityD,aes(x=id_25, color=datatype)) + geom_histogram(fill='white',position='dodge',bins=100)
- train, test 데이터에 고르게 분포
id_26
summary(identityD$id_26) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
100.0 121.0 147.0 150.9 173.8 216.0 275930 round(NROW(which(is.na(identityD$id_26)))/NROW(identityD),2)[1] 0.96- NA가 무려 96%…?????
table(identityD$id_26)
100 101 102 103 104 105 106 107 108 109 110 111 112 113 114
891 1 583 11 1 1 44 22 3 3 14 25 1 3 19
115 116 117 118 119 120 121 122 123 124 125 126 127 128 129
1 5 78 12 669 4 176 6 2 4 2 15 1 5 16
130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
11 10 1 53 39 8 20 441 40 1 2 11 1126 56 24
145 146 147 149 150 152 153 154 155 156 157 158 159 160 161
2 179 547 67 48 28 3 1 3 42 13 4 19 1 1576
162 163 164 165 166 167 168 169 170 171 172 173 174 175 176
89 45 9 2 18 3 74 410 2 9 1 1 3 1 3
177 178 179 180 181 182 183 184 185 186 187 188 189 190 191
36 18 2 13 1 225 15 1196 19 11 1 3 16 135 9
192 193 194 195 196 197 198 199 200 201 202 203 204 205 206
3 2 120 2 1 3 1 34 3 7 6 10 20 11 5
207 208 209 210 211 212 213 214 215 216
1 3 3 1 9 15 5 1 228 352 - 100 ~ 216(117개) 에서 148,151(2개) 빠진 115개 level… 서열척도? 등간척도? 비율척도?
ggplot(identityD,aes(x=id_26, color=datatype)) + geom_histogram(fill='white',position='dodge',bins=100)
- train, test 데이터에 약간 고르게 분포
id_27
summary(identityD$id_27) Found NotFound NA's
10214 17 275909 round(NROW(which(is.na(identityD$id_27)))/NROW(identityD),2)[1] 0.96- NA가 무려 96%…?????
table(ifelse(is.na(identityD$id_27),'missing',as.character(identityD$id_27)),identityD$datatype)
train test
Found 5155 5059
missing 139064 136845
NotFound 14 3- train, test 데이터에 고르게 분포 -> 근데 클래스 불균형 너무 심함.
Found->1, NotFound->0 으로 변환
identityD$id_27 <- ifelse(as.character(identityD$id_27)=='Found',1,
ifelse(as.character(identityD$id_27)=='NotFound',0,NA))id_28
summary(identityD$id_28) Found New NA's
151813 125943 8384 round(NROW(which(is.na(identityD$id_28)))/NROW(identityD),2)[1] 0.03- NA가 3% 정도…
round(prop.table(table(ifelse(is.na(identityD$id_28),'missing',as.character(identityD$id_28)),identityD$datatype)),2)
train test
Found 0.27 0.26
missing 0.01 0.02
New 0.23 0.21- train, test 데이터에 고르게 분포
Found->1, NotFound->0 으로 변환
identityD$id_28 <- ifelse(as.character(identityD$id_28)=='Found',1,
ifelse(as.character(identityD$id_28)=='New',0,NA))id_29
summary(identityD$id_29) Found NotFound NA's
149264 128492 8384 round(NROW(which(is.na(identityD$id_29)))/NROW(identityD),2)[1] 0.03- NA가 3% 정도…
round(prop.table(table(ifelse(is.na(identityD$id_29),'missing',as.character(identityD$id_29)),identityD$datatype)),2)
train test
Found 0.26 0.26
missing 0.01 0.02
NotFound 0.23 0.22- train, test 데이터에 고르게 분포
Found->1, NotFound->0 으로 변환
identityD$id_29 <- ifelse(as.character(identityD$id_29)=='Found',1,
ifelse(as.character(identityD$id_29)=='New',0,NA))id_30
str(identityD$id_30) Factor w/ 87 levels "Android","Android 4.4.2",..: 8 28 NA NA 45 70 NA 1 NA NA ...- os 인듯 -> level(현재 87개) 줄일 수 있을듯
round(NROW(which(is.na(identityD$id_30)))/NROW(identityD),2)[1] 0.48- NA가 무려 48%…
level이 너무 많으므로 합치기
tmp <- identityD %>% group_by(id_30) %>%
summarise(n=n(),prop=round(n/NROW(identityD),3)) %>% arrange(desc(prop))
tmp# A tibble: 88 x 3
id_30 n prop
<fct> <int> <dbl>
1 <NA> 137916 0.482
2 Windows 10 42170 0.147
3 Windows 7 23478 0.082
4 iOS 12.1.0 6349 0.022
5 Mac OS X 10_12_6 3884 0.014
6 iOS 11.1.2 3776 0.013
7 iOS 11.2.1 3824 0.013
8 Mac OS X 10_11_6 3802 0.013
9 Android 7.0 3573 0.012
10 iOS 11.4.1 3539 0.012
# … with 78 more rows- 형태보니까 ’ ’로 한번 자르고 앞에꺼 하면 될듯.
identityD$id_30 <- as.character(identityD$id_30)
identityD$id_30 <- stringr::str_split(string=identityD$id_30, pattern=' ', simplify=T)[,1]
table(identityD$id_30)
Android func iOS Linux Mac other Windows
11783 21 38502 2488 25634 19 69777 - 여기서 ‘Android’, ‘iOS’, ‘Mac’, ‘Windows’, ‘etc’, NA로(6 levels)
identityD$id_30 <- as.factor(ifelse(is.na(identityD$id_30),NA,
ifelse(identityD$id_30 %in% c('func','Linux','other'),'etc',identityD$id_30)))
round(with(identityD,prop.table(table(id_30,datatype))),2) datatype
id_30 train test
Android 0.04 0.04
etc 0.01 0.01
iOS 0.13 0.13
Mac 0.09 0.08
Windows 0.25 0.22- train, test 데이터에 고르게 분포
id_31
str(identityD$id_30) Factor w/ 5 levels "Android","etc",..: 1 3 NA NA 4 5 NA 1 NA NA ...- 인터넷 검색 사이트 인듯 -> level(현재 172개) 줄일 수 있을듯
round(NROW(which(is.na(identityD$id_31)))/NROW(identityD),2)[1] 0.03- NA가 3% 정도…
level이 너무 많으므로 합치기
tmp <- identityD %>% group_by(id_31) %>%
summarise(n=n(),prop=round(n/NROW(identityD),3)) %>% arrange(desc(prop))
tmp# A tibble: 173 x 3
id_31 n prop
<fct> <int> <dbl>
1 mobile safari 11.0 23655 0.083
2 chrome 63.0 22168 0.077
3 chrome 70.0 16054 0.056
4 ie 11.0 for desktop 14203 0.05
5 mobile safari 12.0 13098 0.046
6 mobile safari generic 11474 0.04
7 chrome 71.0 9489 0.033
8 <NA> 9233 0.032
9 chrome 69.0 8294 0.029
10 safari generic 8195 0.029
# … with 163 more rows- 형태보니까 ’ ’로 한번 자르고 앞에 2개 보면 될듯.
identityD$id_31 <- as.character(identityD$id_31)
tmp <- stringr::str_split(string=identityD$id_31, pattern=' ', simplify=T)[,1:2]
identityD$id_31 <- ifelse(tmp[,2]=='safari','safari',tmp[,1])
sort(table(identityD$id_31),decreasing=T)
chrome safari ie firefox
155502 69866 15603 14388
edge samsung google android
11985 4584 2146 1051
opera other Samsung/SM-G532M Generic/Android
843 340 150 138
facebook Samsung/SM-G531H Microsoft/Windows silk
123 52 25 19
mobile ZTE/Blade uc comodo
18 9 8 6
line maxthon aol icedragon
6 6 5 5
Mozilla/Firefox palemoon Lanix/Ilium puffin
5 4 3 2
waterfox blackberry BLU/Dash Cherry
2 1 1 1
chromium cyberfox Inco/Minion iron
1 1 1 1
LG/K-200 M4Tel/M4 Nokia/Lumia rim
1 1 1 1
Samsung/SCH seamonkey
1 1 - 여기서 ‘chrome’, ‘safari’, ‘ie’, ‘firefox’, ‘edge’, ‘etc’, NA로(7 levels)
identityD$id_31 <- as.factor(ifelse(is.na(identityD$id_31),NA,
ifelse(!(identityD$id_31 %in% c('chrome','safari','ie','firefox','edge')),
'etc',identityD$id_31)))
round(with(identityD,prop.table(table(id_31,datatype))),2) datatype
id_31 train test
chrome 0.27 0.29
edge 0.02 0.02
etc 0.01 0.02
firefox 0.03 0.03
ie 0.04 0.02
safari 0.13 0.12- train, test 데이터에 고르게 분포
id_32
summary(identityD$id_32) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 24.00 24.00 26.37 32.00 48.00 137883 round(NROW(which(is.na(identityD$id_32)))/NROW(identityD),2)[1] 0.48- NA가 무려 48%…
table(identityD$id_32)
0 8 16 24 32 48
6 11 121 104040 44077 2 - seq(0,48,8) 로(6개)… 메모리인듯
등간척도라고 가정하고 0~5 값을 가지는 numeric변수로 변환
identityD$id_32 <- ifelse(identityD$id_32==0,0,
ifelse(identityD$id_32==8,1,
ifelse(identityD$id_32==16,2,
ifelse(identityD$id_32==24,3,
ifelse(identityD$id_32==32,4,
ifelse(identityD$id_32==48,5,NA))))))round(with(identityD,prop.table(table(id_32,datatype))),2) datatype
id_32 train test
0 0.00 0.00
1 0.00 0.00
2 0.00 0.00
3 0.36 0.34
4 0.16 0.13
5 0.00 0.00- train, test 데이터에 고르게 분포
id_33
summary(identityD$id_33)1920x1080 1366x768 1334x750 2208x1242 1440x900 1600x900 2048x1536
33742 15046 11550 9113 8127 6603 6556
2436x1125 2560x1600 1280x800 2880x1800 2560x1440 1680x1050 1280x1024
4230 3871 3704 3644 3325 3176 3071
1136x640 1280x720 1024x768 1920x1200 2001x1125 2220x1080 2220x1081
2825 2407 2051 1892 1875 1430 1061
3360x2100 5120x2880 1366x767 2732x2048 3840x2160 2736x1824 2224x1668
870 866 836 791 588 567 551
2688x1242 4096x2304 1360x768 855x480 1919x1080 2160x1440 2961x1442
469 460 454 405 315 285 277
1921x1081 3200x1800 1919x1079 1024x600 1792x828 1365x768 2560x1080
275 261 258 250 232 217 208
3000x2000 2562x1442 1280x768 3440x1440 1600x1200 2400x1350 1280x1025
190 171 167 166 165 153 143
1152x864 960x540 1920x1079 2960x1440 1600x899 2048x1152 2880x1442
140 137 130 117 105 96 92
1536x864 1280x1023 801x480 1599x900 2880x1440 2112x1188 1152x720
85 84 83 80 80 75 72
1280x960 2672x1440 1502x844 1024x767 1281x721 1920x1281 1364x768
67 65 63 59 58 57 52
2160x1081 2256x1504 2400x1600 3840x2400 1920x1081 1599x899 2880x1620
52 51 51 51 49 47 45
1679x1049 1624x750 1229x691 1728x972 2049x1536 1727x971 1279x1023
44 44 43 40 37 35 31
2048x1280 1023x768 1024x640 2161x1080 1279x1024 1707x960 2049x1152
29 27 27 27 26 26 26
2160x1080 2735x1823 1440x810 1440x720 2388x1668 3240x2160 1400x1050
26 26 25 24 23 22 21
(Other) NA's
1299 142180 - 해상도 인듯 -> level(현재 461개) 줄일 수 있거나 가로, 세로로 쪼갤 수 있을듯
round(NROW(which(is.na(identityD$id_33)))/NROW(identityD),2)[1] 0.5- NA가 무려 50% 정도…
’x’로 한번 자르고 numeric변수 2개 만들기
identityD$id_33 <- as.character(identityD$id_33)
tmp <- stringr::str_split(string=identityD$id_33, pattern='x', simplify=T)[,1:2]
identityD$id_33_1 <- as.numeric(tmp[,1])
identityD$id_33_2 <- as.numeric(tmp[,2])
identityD$id_33 <- NULLggplot(identityD,aes(x=id_33_1, color=datatype)) + geom_histogram(fill='white',position='dodge',bins=100)
ggplot(identityD,aes(x=id_33_2, color=datatype)) + geom_histogram(fill='white',position='dodge',bins=100)
- train, test 데이터에 약간 고르게 분포
id_34
summary(identityD$id_34)match_status:-1 match_status:0 match_status:1 match_status:2
3 415 17377 132185
NA's
136160 - match_status 이게뭐지??? html 크롤링할 때 나오는건가
round(NROW(which(is.na(identityD$id_34)))/NROW(identityD),2)[1] 0.48- NA가 무려 48% 정도…
table(ifelse(is.na(identityD$id_34),'missing',as.character(identityD$id_34)),identityD$datatype)
train test
match_status:-1 3 0
match_status:0 415 0
match_status:1 17376 1
match_status:2 60011 72174
missing 66428 69732- 클래스 불균형이 너무 심함
-1,0은 합치자
identityD$id_34 <- as.character(identityD$id_34)
identityD$id_34 <- as.factor(ifelse(identityD$id_34 %in% c('match_status:-1','match_status:0'),'<=0',
ifelse(identityD$id_34=='match_status:1',1,
ifelse(identityD$id_34=='match_status:2',2,NA))))round(with(identityD,prop.table(table(id_34,datatype))),2) datatype
id_34 train test
<=0 0.00 0.00
1 0.12 0.00
2 0.40 0.48- train, test 데이터에 약간 고르게 분포
id_35, id_36, id_37, id_38
sapply(identityD[,c('id_35','id_36','id_37','id_38')],summary) id_35 id_36 id_37 id_38
Mode "logical" "logical" "logical" "logical"
FALSE "128498" "267353" "62813" "168980"
TRUE "149464" "10609" "215149" "108982"
NA's "8178" "8178" "8178" "8178" - 모두 True or False 값을 가지고 있음
sapply(identityD[,c('id_35','id_36','id_37','id_38')],
function(x){round(NROW(which(is.na(x)))/NROW(identityD),2)})id_35 id_36 id_37 id_38
0.03 0.03 0.03 0.03 round(NROW(which(is.na(identityD$id_35)))/NROW(identityD),2)[1] 0.03- 모두 NA가 3% 정도…
수치형(T->1, F->0)으로 변환
for(i in c('id_35','id_36','id_37','id_38'))
identityD[,i] <- as.numeric(identityD[,i])for(i in c('id_35','id_36','id_37','id_38'))
print(round(prop.table(table(ifelse(is.na(identityD[,i]),'missing',identityD[,i]),identityD$datatype)),2))
train test
0 0.22 0.23
1 0.27 0.25
missing 0.01 0.02
train test
0 0.47 0.47
1 0.02 0.01
missing 0.01 0.02
train test
0 0.11 0.11
1 0.39 0.37
missing 0.01 0.02
train test
0 0.26 0.33
1 0.23 0.15
missing 0.01 0.02- 모두 train, test 데이터에 고르게 분포
(1)-2. Numeric Features
id_01
summary(identityD$id_01) Min. 1st Qu. Median Mean 3rd Qu. Max.
-100.00 -10.00 -5.00 -10.74 -5.00 0.00 - NA는 없음.
구체적인 값
table(identityD$id_01)
-100 -99 -96 -95 -94 -93 -92 -91 -90 -89
1706 2 4 698 1 2 3 1 323 1
-88 -87 -86 -85 -84 -83 -82 -80 -78 -77
4 5 1 249 1 1 1 373 1 1
-76 -75 -73 -72 -71 -70 -69 -66 -65 -64
3 196 1 3 4 205 1 3 361 7
-63 -62 -61 -60 -58 -57 -56 -55 -54 -53
4 6 14 1596 3 3 4 576 2 2
-52 -51 -50 -49 -48 -47 -46 -45 -44 -43
2 3 1600 1 2 8 6 6695 4 2
-42 -41 -40 -39 -38 -37 -36 -35 -34 -33
4 1 2409 2 6 5 1 2809 12 11
-32 -31 -30 -29 -28 -27 -26 -25 -24 -23
12 17 1754 7 9 11 30 9640 1 9
-22 -21 -20 -19 -18 -17 -16 -15 -14 -13
6 23 22341 12 32 20 38 12722 18 10
-12 -11 -10 -9 -8 -7 -6 -5 0
24 39 23050 5 7 18 26 164749 31555 - seq(-100,0,1)의 범위(정수)를 갖고 있음, 범주형이라 치면 level 89개 존재(-98,-97,-81,-79,-74,-68,-67,-59,-4,-3,-2,-1 빼고)
분포비교 : train 데이터 vs test 데이터
tapply(identityD$id_01,identityD$datatype,summary) # 수치값 비교$train
Min. 1st Qu. Median Mean 3rd Qu. Max.
-100.00 -10.00 -5.00 -10.17 -5.00 0.00
$test
Min. 1st Qu. Median Mean 3rd Qu. Max.
-100.00 -12.50 -5.00 -11.33 -5.00 0.00 # 히스토그램, 박스플롯 비교
p1 <- ggplot(identityD,aes(y=id_01, x=datatype, color=datatype)) + geom_boxplot(show.legend=F)
p2 <- ggplot(identityD,aes(x=id_01, color=datatype)) +
geom_histogram(fill='white',position='dodge',bins=100) + theme(legend.position=c(0.5,0.5))
gridExtra::grid.arrange(p1,p2,ncol=2)
# 윌콕슨 부호순위 검정(두 그룹간 차이가 있는지 확인)
wilcox.test(identityD$id_01[identityD$datatype=='train'],identityD$id_01[identityD$datatype=='test']) # p-value가 0.05보다 작으므로 차이 X
Wilcoxon rank sum test with continuity correction
data: identityD$id_01[identityD$datatype == "train"] and identityD$id_01[identityD$datatype == "test"]
W = 10974000000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0- train, test 데이터에 고르게 분포
id_02
summary(identityD$id_02) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1 65946 129158 183562 245795 999869 8292 round(NROW(which(is.na(identityD$id_02)))/NROW(identityD),2)[1] 0.03- NA는 약 3%정도…
- 정수임
분포비교 : train 데이터 vs test 데이터
tapply(identityD$id_02,identityD$datatype,summary) # 수치값 비교$train
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1 67992 125800 174717 228749 999595 3361
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
2 63340 133190 192659 265718 999869 4931 # 히스토그램, 박스플롯 비교
p1 <- ggplot(identityD,aes(y=id_02, x=datatype, color=datatype)) + geom_boxplot(show.legend=F)
p2 <- ggplot(identityD,aes(x=id_02, color=datatype)) +
geom_histogram(fill='white',position='dodge',bins=100,alpha=0.9) + theme(legend.position=c(0.5,0.5))
gridExtra::grid.arrange(p1,p2,ncol=2)
# 윌콕슨 부호순위 검정(두 그룹간 차이가 있는지 확인)
wilcox.test(identityD$id_02[identityD$datatype=='train'],identityD$id_02[identityD$datatype=='test']) # p-value가 0.05보다 작으므로 차이 X
Wilcoxon rank sum test with continuity correction
data: identityD$id_02[identityD$datatype == "train"] and identityD$id_02[identityD$datatype == "test"]
W = 9378900000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0- train, test 데이터에 고르게 분포
id_03
summary(identityD$id_03) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-13.00 0.00 0.00 0.06 0.00 11.00 153335 round(NROW(which(is.na(identityD$id_03)))/NROW(identityD),2)[1] 0.54- NA가 무려 54%….
구체적인 값
table(identityD$id_03)
-13 -12 -11 -10 -9 -8 -7 -6 -5 -4
3 19 20 26 27 41 54 84 88 56
-3 -2 -1 0 1 2 3 4 5 6
12 58 18 127811 1645 806 1442 184 264 134
7 8 9 10 11
6 1 4 1 1 - seq(-13,11,1)의 범위(정수)를 갖고 있음, 범주형이라 치면 level 25개 존재
분포비교 : train 데이터 vs test 데이터
# 수치값 비교
tapply(identityD$id_03,identityD$datatype,summary)$train
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-13.00 0.00 0.00 0.06 0.00 10.00 77909
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-12.00 0.00 0.00 0.05 0.00 11.00 75426 tmp <- tapply(identityD$id_03,identityD$datatype,table)
(tmp <- merge(data.frame(tmp$train),data.frame(tmp$test),by='Var1',all=T, suffixes=c('_train','_test'))) Var1 Freq_train Freq_test
1 -13 3 NA
2 -12 3 16
3 -11 6 14
4 -10 17 9
5 -9 6 21
6 -8 14 27
7 -7 21 33
8 -6 31 53
9 -5 33 55
10 -4 21 35
11 -3 8 4
12 -2 12 46
13 -1 12 6
14 0 63903 63908
15 1 863 782
16 2 421 385
17 3 668 774
18 4 100 84
19 5 109 155
20 6 64 70
21 7 4 2
22 8 1 NA
23 9 3 1
24 10 1 NA
25 11 NA 1# 윌콕슨 부호순위 검정(두 그룹간 차이가 있는지 확인)
wilcox.test(identityD$id_03[identityD$datatype=='train'],identityD$id_03[identityD$datatype=='test']) # p-value가 0.05보다 크네...?
Wilcoxon rank sum test with continuity correction
data: identityD$id_03[identityD$datatype == "train"] and identityD$id_03[identityD$datatype == "test"]
W = 2208200000, p-value = 0.1192
alternative hypothesis: true location shift is not equal to 0- train, test 데이터에 고르게 분포한 줄 알았으나 wilcox.test에서는 다르게 분포한다고 나옴…
id_04
summary(identityD$id_04) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-28.00 0.00 0.00 -0.07 0.00 0.00 153335 round(NROW(which(is.na(identityD$id_04)))/NROW(identityD),2)[1] 0.54- NA가 무려 54%….
table(identityD$id_04)
-28 -19 -13 -12 -11 -10 -9 -8 -7 -6
2 1 36 111 75 63 133 100 67 297
-5 -4 -3 -2 -1 0
317 88 26 31 68 131390 - [-28, -19, seq(-13,0,1)]의 범위(정수)를 갖고 있음, 범주형이라 치면 level 16개 존재
분포비교 : train 데이터 vs test 데이터
# 수치값 비교
tapply(identityD$id_04,identityD$datatype,summary)$train
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-28.00 0.00 0.00 -0.06 0.00 0.00 77909
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-19.00 0.00 0.00 -0.09 0.00 0.00 75426 tmp <- tapply(identityD$id_04,identityD$datatype,table)
(tmp <- merge(data.frame(tmp$train),data.frame(tmp$test),by='Var1',all=T, suffixes=c('_train','_test'))) Var1 Freq_train Freq_test
1 -28 2 NA
2 -13 24 12
3 -12 34 77
4 -11 35 40
5 -10 30 33
6 -9 26 107
7 -8 64 36
8 -7 21 46
9 -6 98 199
10 -5 132 185
11 -4 51 37
12 -3 10 16
13 -2 15 16
14 -1 43 25
15 0 65739 65651
16 -19 NA 1# 윌콕슨 부호순위 검정(두 그룹간 차이가 있는지 확인)
wilcox.test(identityD$id_04[identityD$datatype=='train'],identityD$id_04[identityD$datatype=='test']) # p-value가 0.05보다 작으므로 차이 X
Wilcoxon rank sum test with continuity correction
data: identityD$id_04[identityD$datatype == "train"] and identityD$id_04[identityD$datatype == "test"]
W = 2212700000, p-value = 0.00000000007027
alternative hypothesis: true location shift is not equal to 0- train, test 데이터에 고르게 분포
id_05
summary(identityD$id_05) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-81.000 0.000 0.000 1.432 1.000 52.000 14525 round(NROW(which(is.na(identityD$id_05)))/NROW(identityD),2)[1] 0.05- NA가 5% 정도…
- 정수임
분포비교 : train 데이터 vs test 데이터
tapply(identityD$id_05,identityD$datatype,summary) # 수치값 비교$train
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-72.000 0.000 0.000 1.616 1.000 52.000 7368
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-81.000 0.000 0.000 1.246 1.000 52.000 7157 # 히스토그램 비교
ggplot(identityD,aes(x=id_05, color=datatype)) +
geom_histogram(fill='white',position='dodge',bins=100) +
theme(legend.position=c(0.5,0.5))
# 윌콕슨 부호순위 검정(두 그룹간 차이가 있는지 확인)
wilcox.test(identityD$id_05[identityD$datatype=='train'],identityD$id_05[identityD$datatype=='test']) # p-value가 0.05보다 작으므로 차이 X
Wilcoxon rank sum test with continuity correction
data: identityD$id_05[identityD$datatype == "train"] and identityD$id_05[identityD$datatype == "test"]
W = 9567700000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0- train, test 데이터에 고르게 분포
id_06
summary(identityD$id_06) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-100.000 -6.000 0.000 -6.751 0.000 0.000 14525 round(NROW(which(is.na(identityD$id_06)))/NROW(identityD),2)[1] 0.05- NA가 5% 정도…
- 정수임
분포비교 : train 데이터 vs test 데이터
tapply(identityD$id_06,identityD$datatype,summary) # 수치값 비교$train
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-100.000 -6.000 0.000 -6.699 0.000 0.000 7368
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-100.000 -6.000 0.000 -6.804 0.000 0.000 7157 # 히스토그램 비교
ggplot(identityD,aes(x=id_06, color=datatype)) +
geom_histogram(position='dodge',bins=100,alpha=0.2) + theme(legend.position=c(0.5,0.5))
# 윌콕슨 부호순위 검정(두 그룹간 차이가 있는지 확인)
wilcox.test(identityD$id_06[identityD$datatype=='train'],identityD$id_06[identityD$datatype=='test']) # p-value가 0.05보다 작으므로 차이 X
Wilcoxon rank sum test with continuity correction
data: identityD$id_06[identityD$datatype == "train"] and identityD$id_06[identityD$datatype == "test"]
W = 9548600000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0- train, test 데이터에 고르게 분포
id_07
summary(identityD$id_07) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-46.00 4.00 13.00 12.89 21.00 61.00 275926 round(NROW(which(is.na(identityD$id_07)))/NROW(identityD),2)[1] 0.96- NA가 무려 96%…
- 정수임
분포비교 : train 데이터 vs test 데이터
tapply(identityD$id_07,identityD$datatype,summary) # 수치값 비교$train
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-46.00 5.00 14.00 13.29 22.00 61.00 139078
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-41.00 3.00 12.00 12.49 21.00 59.00 136848 # 히스토그램, 박스플롯 비교
p1 <- ggplot(identityD,aes(y=id_07, x=datatype, color=datatype)) + geom_boxplot(show.legend=F)
p2 <- ggplot(identityD,aes(x=id_07, color=datatype)) +
geom_histogram(fill='white',position='dodge',bins=100,alpha=0.9) + theme(legend.position=c(0.9,0.5))
gridExtra::grid.arrange(p1,p2,ncol=2)
# 윌콕슨 부호순위 검정(두 그룹간 차이가 있는지 확인)
wilcox.test(identityD$id_07[identityD$datatype=='train'],identityD$id_07[identityD$datatype=='test']) # p-value가 0.05보다 작으므로 차이 X
Wilcoxon rank sum test with continuity correction
data: identityD$id_07[identityD$datatype == "train"] and identityD$id_07[identityD$datatype == "test"]
W = 13791000, p-value = 0.0000004497
alternative hypothesis: true location shift is not equal to 0- train, test 데이터에 고르게 분포
id_08
summary(identityD$id_08) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-100.0 -47.0 -34.0 -37.6 -23.0 0.0 275926 round(NROW(which(is.na(identityD$id_08)))/NROW(identityD),2)[1] 0.96- NA가 무려 96%…
- 정수임
분포비교 : train 데이터 vs test 데이터
tapply(identityD$id_08,identityD$datatype,summary) # 수치값 비교$train
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-100.0 -48.0 -34.0 -38.6 -23.0 0.0 139078
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-100.00 -46.00 -33.00 -36.58 -23.00 0.00 136848 # 히스토그램, 박스플롯 비교
p1 <- ggplot(identityD,aes(y=id_08, x=datatype, color=datatype)) + geom_boxplot(show.legend=F)
p2 <- ggplot(identityD,aes(x=id_08, color=datatype)) +
geom_histogram(fill='white',position='dodge',bins=100,alpha=0.9) + theme(legend.position=c(0.9,0.9))
gridExtra::grid.arrange(p1,p2,ncol=2)
# 윌콕슨 부호순위 검정(두 그룹간 차이가 있는지 확인)
wilcox.test(identityD$id_08[identityD$datatype=='train'],identityD$id_09[identityD$datatype=='test']) # p-value가 0.05보다 작으므로 차이 X
Wilcoxon rank sum test with continuity correction
data: identityD$id_08[identityD$datatype == "train"] and identityD$id_09[identityD$datatype == "test"]
W = 9399800, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0- train, test 데이터에 고르게 분포
id_09
summary(identityD$id_09) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-36.00 0.00 0.00 0.08 0.00 25.00 136876 round(NROW(which(is.na(identityD$id_09)))/NROW(identityD),2)[1] 0.48- NA가 무려 48%…
- 정수임
분포비교 : train 데이터 vs test 데이터
tapply(identityD$id_09,identityD$datatype,summary) # 수치값 비교$train
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-36.00 0.00 0.00 0.09 0.00 25.00 69307
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-32.00 0.00 0.00 0.08 0.00 16.00 67569 tmp <- tapply(identityD$id_09,identityD$datatype,table)
(tmp <- merge(data.frame(tmp$train),data.frame(tmp$test),by='Var1',all=T, suffixes=c('_train','_test'))) Var1 Freq_train Freq_test
1 -36 1 NA
2 -31 3 1
3 -30 1 NA
4 -26 2 2
5 -24 1 NA
6 -23 4 NA
7 -22 4 NA
8 -21 3 NA
9 -20 1 1
10 -19 2 NA
11 -18 2 NA
12 -17 4 NA
13 -15 2 NA
14 -14 1 NA
15 -13 4 2
16 -12 4 16
17 -11 18 16
18 -10 39 17
19 -9 27 34
20 -8 37 50
21 -7 39 45
22 -6 66 80
23 -5 60 88
24 -4 42 96
25 -3 31 25
26 -2 27 40
27 -1 38 23
28 0 70378 70014
29 1 1616 1381
30 2 773 731
31 3 966 1008
32 4 270 180
33 5 207 243
34 6 145 140
35 7 33 23
36 8 23 20
37 9 16 12
38 10 11 9
39 11 6 9
40 12 6 4
41 13 4 5
42 14 1 1
43 15 3 1
44 16 3 1
45 17 1 NA
46 25 1 NA
47 -32 NA 9
48 -29 NA 1
49 -28 NA 6
50 -27 NA 3
51 -16 NA 1# 윌콕슨 부호순위 검정(두 그룹간 차이가 있는지 확인)
wilcox.test(identityD$id_09[identityD$datatype=='train'],identityD$id_09[identityD$datatype=='test']) # p-value가 0.05보다 작으므로 차이 X
Wilcoxon rank sum test with continuity correction
data: identityD$id_09[identityD$datatype == "train"] and identityD$id_09[identityD$datatype == "test"]
W = 2798700000, p-value = 0.00005065
alternative hypothesis: true location shift is not equal to 0- train, test 데이터에 고르게 분포
id_10
summary(identityD$id_10) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-100.00 0.00 0.00 -0.27 0.00 0.00 136876 round(NROW(which(is.na(identityD$id_10)))/NROW(identityD),2)[1] 0.48- NA가 무려 48%…
- 정수임
분포비교 : train 데이터 vs test 데이터
tapply(identityD$id_10,identityD$datatype,summary) # 수치값 비교$train
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-100.0 0.0 0.0 -0.3 0.0 0.0 69307
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-100.00 0.00 0.00 -0.25 0.00 0.00 67569 tmp <- tapply(identityD$id_10,identityD$datatype,table)
(tmp <- merge(data.frame(tmp$train),data.frame(tmp$test),by='Var1',all=T, suffixes=c('_train','_test'))) Var1 Freq_train Freq_test
1 -100 20 1
2 -68 2 NA
3 -65 1 NA
4 -64 1 11
5 -60 4 NA
6 -59 1 NA
7 -58 1 2
8 -57 1 NA
9 -56 4 NA
10 -55 1 1
11 -54 7 NA
12 -53 1 NA
13 -51 1 NA
14 -50 1 3
15 -49 4 1
16 -47 6 NA
17 -45 1 1
18 -44 1 5
19 -43 5 NA
20 -42 1 NA
21 -41 1 NA
22 -40 1 5
23 -39 3 1
24 -38 5 1
25 -37 2 1
26 -36 3 NA
27 -35 4 NA
28 -34 3 1
29 -33 10 2
30 -32 2 4
31 -31 10 3
32 -30 10 8
33 -29 13 3
34 -28 25 9
35 -27 7 4
36 -26 7 8
37 -25 13 5
38 -24 15 16
39 -23 7 1
40 -22 14 3
41 -21 16 1
42 -20 6 8
43 -19 11 6
44 -18 11 6
45 -17 7 7
46 -16 33 8
47 -15 18 4
48 -14 17 11
49 -13 89 48
50 -12 118 168
51 -11 127 152
52 -10 115 107
53 -9 118 189
54 -8 147 102
55 -7 109 191
56 -6 295 306
57 -5 247 256
58 -4 87 64
59 -3 24 26
60 -2 33 26
61 -1 200 263
62 0 72879 72277
63 -88 NA 6
64 -67 NA 1
65 -63 NA 1
66 -48 NA 2
67 -46 NA 2# 윌콕슨 부호순위 검정(두 그룹간 차이가 있는지 확인)
wilcox.test(identityD$id_10[identityD$datatype=='train'],identityD$id_10[identityD$datatype=='test']) # p-value가 0.05보다 크네...?
Wilcoxon rank sum test with continuity correction
data: identityD$id_10[identityD$datatype == "train"] and identityD$id_10[identityD$datatype == "test"]
W = 2785900000, p-value = 0.6888
alternative hypothesis: true location shift is not equal to 0- train, test 데이터에 고르게 분포한 줄 알았으나 wilcox.test에서는 다르게 분포한다고 나옴…
id_11
summary(identityD$id_11) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
90.00 100.00 100.00 99.75 100.00 100.00 8384 round(NROW(which(is.na(identityD$id_11)))/NROW(identityD),2)[1] 0.03- NA가 3% 정도…
- 90~100사이의 실수임
분포비교 : train 데이터 vs test 데이터
tapply(identityD$id_11,identityD$datatype,summary) # 수치값 비교$train
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
90.00 100.00 100.00 99.75 100.00 100.00 3255
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
90.00 100.00 100.00 99.75 100.00 100.00 5129 # 히스토그램 비교
ggplot(identityD,aes(x=id_11, color=datatype)) +
geom_histogram(fill='white',position='dodge',bins=100,alpha=0.9) + theme(legend.position=c(0.5,0.5))
# 윌콕슨 부호순위 검정(두 그룹간 차이가 있는지 확인)
wilcox.test(identityD$id_11[identityD$datatype=='train'],identityD$id_11[identityD$datatype=='test']) # p-value가 0.05보다 크네...?
Wilcoxon rank sum test with continuity correction
data: identityD$id_11[identityD$datatype == "train"] and identityD$id_11[identityD$datatype == "test"]
W = 9631800000, p-value = 0.2503
alternative hypothesis: true location shift is not equal to 0- train, test 데이터에 고르게 분포한 줄 알았으나 wilcox.test에서는 다르게 분포한다고 나옴…
(2) transactionD : train_t + test_t
transD <- rbind(data.frame(train_t,datatype='train'),data.frame(test_t,isFraud=NA,datatype='test'))
str(transD) # 1097231 x 395'data.frame': 1097231 obs. of 395 variables:
$ TransactionID : int 2987000 2987001 2987002 2987003 2987004 2987005 2987006 2987007 2987008 2987009 ...
$ isFraud : int 0 0 0 0 0 0 0 0 0 0 ...
$ TransactionDT : int 86400 86401 86469 86499 86506 86510 86522 86529 86535 86536 ...
$ TransactionAmt: num 68.5 29 59 50 50 ...
$ ProductCD : Factor w/ 5 levels "C","H","R","S",..: 5 5 5 5 2 5 5 5 2 5 ...
$ card1 : int 13926 2755 4663 18132 4497 5937 12308 12695 2803 17399 ...
$ card2 : num NA 404 490 567 514 555 360 490 100 111 ...
$ card3 : num 150 150 150 150 150 150 150 150 150 150 ...
$ card4 : Factor w/ 4 levels "american express",..: 2 3 4 3 3 4 4 4 4 3 ...
$ card5 : num 142 102 166 117 102 226 166 226 226 224 ...
$ card6 : Factor w/ 4 levels "charge card",..: 2 2 3 3 2 3 3 3 3 3 ...
$ addr1 : num 315 325 330 476 420 272 126 325 337 204 ...
$ addr2 : num 87 87 87 87 87 87 87 87 87 87 ...
$ dist1 : num 19 NA 287 NA NA 36 0 NA NA 19 ...
$ dist2 : num NA NA NA NA NA NA NA NA NA NA ...
$ P_emaildomain : Factor w/ 60 levels "aim.com","anonymous.com",..: NA 17 36 54 17 17 54 30 2 54 ...
$ R_emaildomain : Factor w/ 60 levels "aim.com","anonymous.com",..: NA NA NA NA NA NA NA NA NA NA ...
$ C1 : num 1 1 1 2 1 1 1 1 1 2 ...
$ C2 : num 1 1 1 5 1 1 1 1 1 2 ...
$ C3 : num 0 0 0 0 0 0 0 0 0 0 ...
$ C4 : num 0 0 0 0 0 0 0 0 0 0 ...
$ C5 : num 0 0 0 0 0 0 0 0 0 0 ...
$ C6 : num 1 1 1 4 1 1 1 1 1 3 ...
$ C7 : num 0 0 0 0 0 0 0 0 0 0 ...
$ C8 : num 0 0 0 0 1 0 0 0 1 0 ...
$ C9 : num 1 0 1 1 0 1 1 0 0 3 ...
$ C10 : num 0 0 0 0 1 0 0 0 1 0 ...
$ C11 : num 2 1 1 1 1 1 1 1 1 1 ...
$ C12 : num 0 0 0 0 0 0 0 0 0 0 ...
$ C13 : num 1 1 1 25 1 1 1 1 1 12 ...
$ C14 : num 1 1 1 1 1 1 1 1 1 2 ...
$ D1 : num 14 0 0 112 0 0 0 0 0 61 ...
$ D2 : num NA NA NA 112 NA NA NA NA NA 61 ...
$ D3 : num 13 NA NA 0 NA NA NA NA NA 30 ...
$ D4 : num NA 0 0 94 NA 0 0 0 NA 318 ...
$ D5 : num NA NA NA 0 NA NA NA NA NA 30 ...
$ D6 : num NA NA NA NA NA NA NA NA NA NA ...
$ D7 : num NA NA NA NA NA NA NA NA NA NA ...
$ D8 : num NA NA NA NA NA NA NA NA NA NA ...
$ D9 : num NA NA NA NA NA NA NA NA NA NA ...
$ D10 : num 13 0 0 84 NA 0 0 0 NA 40 ...
$ D11 : num 13 NA 315 NA NA 0 0 NA NA 302 ...
$ D12 : num NA NA NA NA NA NA NA NA NA NA ...
$ D13 : num NA NA NA NA NA NA NA NA NA NA ...
$ D14 : num NA NA NA NA NA NA NA NA NA NA ...
$ D15 : num 0 0 315 111 NA 0 0 0 NA 318 ...
$ M1 : logi TRUE NA TRUE NA NA TRUE ...
$ M2 : logi TRUE NA TRUE NA NA TRUE ...
$ M3 : logi TRUE NA TRUE NA NA TRUE ...
$ M4 : Factor w/ 3 levels "M0","M1","M2": 3 1 1 1 NA 2 1 1 NA 1 ...
$ M5 : logi FALSE TRUE FALSE TRUE NA FALSE ...
$ M6 : logi TRUE TRUE FALSE FALSE NA TRUE ...
$ M7 : logi NA NA FALSE NA NA NA ...
$ M8 : logi NA NA FALSE NA NA NA ...
$ M9 : logi NA NA FALSE NA NA NA ...
$ V1 : num 1 NA 1 NA NA 1 1 NA NA 1 ...
$ V2 : num 1 NA 1 NA NA 1 1 NA NA 1 ...
$ V3 : num 1 NA 1 NA NA 1 1 NA NA 1 ...
$ V4 : num 1 NA 1 NA NA 1 1 NA NA 1 ...
$ V5 : num 1 NA 1 NA NA 1 1 NA NA 1 ...
$ V6 : num 1 NA 1 NA NA 1 1 NA NA 1 ...
$ V7 : num 1 NA 1 NA NA 1 1 NA NA 1 ...
$ V8 : num 1 NA 1 NA NA 1 1 NA NA 1 ...
$ V9 : num 1 NA 1 NA NA 1 1 NA NA 1 ...
$ V10 : num 0 NA 0 NA NA 0 0 NA NA 0 ...
$ V11 : num 0 NA 0 NA NA 0 0 NA NA 0 ...
$ V12 : num 1 0 1 1 NA 1 1 0 NA 1 ...
$ V13 : num 1 0 1 1 NA 1 1 0 NA 1 ...
$ V14 : num 1 1 1 1 NA 1 1 1 NA 1 ...
$ V15 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V16 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V17 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V18 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V19 : num 1 1 1 1 NA 1 1 1 NA 1 ...
$ V20 : num 1 1 1 1 NA 1 1 1 NA 1 ...
$ V21 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V22 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V23 : num 1 1 1 1 NA 1 1 1 NA 1 ...
$ V24 : num 1 1 1 1 NA 1 1 1 NA 1 ...
$ V25 : num 1 1 1 1 NA 1 1 1 NA 1 ...
$ V26 : num 1 1 1 1 NA 1 1 1 NA 1 ...
$ V27 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V28 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V29 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V30 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V31 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V32 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V33 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V34 : num 0 0 0 0 NA 0 0 0 NA 0 ...
$ V35 : num NA 0 1 1 NA 1 1 0 NA 1 ...
$ V36 : num NA 0 1 1 NA 1 1 0 NA 1 ...
$ V37 : num NA 1 1 1 NA 1 1 1 NA 1 ...
$ V38 : num NA 1 1 1 NA 1 1 1 NA 1 ...
$ V39 : num NA 0 0 0 NA 0 0 0 NA 0 ...
$ V40 : num NA 0 0 0 NA 0 0 0 NA 0 ...
$ V41 : num NA 1 1 1 NA 1 1 1 NA 1 ...
$ V42 : num NA 0 0 0 NA 0 0 0 NA 0 ...
$ V43 : num NA 0 0 0 NA 0 0 0 NA 0 ...
$ V44 : num NA 1 1 1 NA 1 1 1 NA 1 ...
[list output truncated]변수별 보기
(2)-1. Categorical Features
TransactionID
summary(transD$TransactionID) Min. 1st Qu. Median Mean 3rd Qu. Max.
2987000 3261308 3535615 3575333 3895932 4170239 transD %>% group_by(datatype) %>% summarise(n=n(),min=min(TransactionID),max=max(TransactionID))# A tibble: 2 x 4
datatype n min max
<fct> <int> <dbl> <dbl>
1 train 590540 2987000 3577539
2 test 506691 3663549 4170239identityD %>% group_by(datatype) %>% summarise(n=n(),min=min(TransactionID),max=max(TransactionID))# A tibble: 2 x 4
datatype n min max
<fct> <int> <dbl> <dbl>
1 train 144233 2987004 3577534
2 test 141907 3663586 4170239- 7글자…
- id가 연속적이지 X
- train 보다 test가 더 뒤의 id
- 중복된 값 X
- identityD의 id보다 더 범위가 넓음 : identityD에 있는 id는 모두 transD에 있지만 그 역은 X
- 모델링에는 X
isFraud : TARGET 변수(0 or 1)
summary(as.factor(transD$isFraud)) 0 1 NA's
569877 20663 506691 round(prop.table(table(transD$isFraud)),4)
0 1
0.965 0.035 - 1인 경우가 3%정도 밖에 되지 않아서 클래스 불균형 -> resampling 필요할듯
TransactionDT
summary(transD$TransactionDT) Min. 1st Qu. Median Mean 3rd Qu. Max.
86400 6651887 14491621 16403840 26513228 34214345 NROW(unique(transD$TransactionDT)) # 1068035 / 1097231[1] 1068035- timedelta from a given reference datetime (not an actual timestamp)
- NA는 없음.
- 조금 중복된 값 있음
#transD %>% group_by(datatype) %>% summarise(n=n(),min=min(TransactionDT),max=max(TransactionDT))- train보다 test의 TransactionDT가 더 큼
ggplot(transD,aes(x=TransactionDT, color=datatype)) + geom_histogram(alpha=0.5,bins=100)
ProductCD
summary(transD$ProductCD) C H R S W
137785 62397 73346 23046 800657 - NA는 없음
round(prop.table(table(transD$ProductCD, transD$datatype)),3)
train test
C 0.062 0.063
H 0.030 0.027
R 0.034 0.032
S 0.011 0.010
W 0.401 0.329- train, test 데이터에 고르게 분포
- W의 값이 좀 많은것 같음
card1
summary(transD$card1) Min. 1st Qu. Median Mean 3rd Qu. Max.
1000 6019 9749 9926 14248 18397 str(as.factor(transD$card1)) Factor w/ 17091 levels "1000","1001",..: 12697 1727 3598 16831 3435 4846 11108 11489 1772 16116 ...- NA는 없음
- level이 무려 17901개 -> 조정 필요하지만 명목변수 인것 같아 어려울듯
ggplot(transD,aes(x=card1,color=datatype)) + geom_histogram(position='dodge',bins=100,alpha=0.2)
- train, test 데이터에 약간 고르게 분포
card2
summary(transD$card2) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
100.0 210.0 361.0 363.1 512.0 600.0 17587 round(NROW(which(is.na(transD$card2)))/NROW(transD),2)[1] 0.02- NA가 17587개로 전체의 약 2%
str(as.factor(transD$card2)) Factor w/ 501 levels "100","101","102",..: NA 305 391 468 415 456 261 391 1 12 ...- level이 무려 501개
ggplot(transD,aes(x=card2,color=datatype)) + geom_histogram(position='dodge',bins=100,alpha=0.2)
- train, test 데이터에 약간 고르게 분포
card3
summary(transD$card3) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
100.0 150.0 150.0 153.4 150.0 232.0 4567 NROW(which(is.na(transD$card3)))/NROW(transD)[1] 0.004162296- NA가 4567개로 전체의 약 0.4%
str(as.factor(transD$card3)) Factor w/ 133 levels "100","101","102",..: 51 51 51 51 51 51 51 51 51 51 ...- level이 무려 133개
ggplot(transD,aes(x=card3,color=datatype)) + geom_histogram(position='dodge',bins=100,alpha=0.2)
- train, test 데이터에 약간 고르게 분포
card4
summary(transD$card4)american express discover mastercard visa
16009 9524 347386 719649
NA's
4663 NROW(which(is.na(transD$card4)))/NROW(transD)[1] 0.004249789- 카드 종류인듯
- NA가 4663개로 전체의 약 0.4%
round(prop.table(table(ifelse(is.na(transD$card4),'missing',as.character(transD$card4)), transD$datatype)),3)
train test
american express 0.008 0.007
discover 0.006 0.003
mastercard 0.172 0.144
missing 0.001 0.003
visa 0.351 0.305- train, test 데이터에 고르게 분포
- mastercard, visa의 값이 좀 많은것 같음
card5
summary(transD$card5) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
100.0 166.0 226.0 199.7 226.0 237.0 8806 NROW(which(is.na(transD$card5)))/NROW(transD)[1] 0.008025657- NA가 8806개로 전체의 약 0.8%
str(as.factor(transD$card5)) Factor w/ 138 levels "100","101","102",..: 43 3 67 18 3 127 67 127 127 125 ...- level이 무려 138개
ggplot(transD,aes(x=card5,color=datatype)) + geom_histogram(position='dodge',bins=100,alpha=0.2)
- train, test 데이터에 약간 고르게 분포
card6
summary(transD$card6) charge card credit debit debit or credit
16 267648 824959 30
NA's
4578 NROW(which(is.na(transD$card6)))/NROW(transD)[1] 0.004172321- 카드 지불 종류인듯
- NA가 4663개로 전체의 약 0.4%
round(prop.table(table(ifelse(is.na(transD$card6),'missing',as.character(transD$card6)), transD$datatype)),3)
train test
charge card 0.000 0.000
credit 0.136 0.108
debit 0.401 0.351
debit or credit 0.000 0.000
missing 0.001 0.003- train, test 데이터에 고르게 분포
- credit, debit or credit 값이 좀 많은것 같음
addr1
summary(transD$addr1) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
100.0 204.0 299.0 291.2 330.0 540.0 131315 round(NROW(which(is.na(transD$addr1)))/NROW(transD),2)[1] 0.12- NA가 131315개로 전체의 약 12%
str(as.factor(transD$addr1)) Factor w/ 441 levels "100","101","102",..: 216 226 231 377 321 173 27 226 238 105 ...- level이 무려 441개
ggplot(transD,aes(x=addr1,color=datatype)) + geom_histogram(position='dodge',bins=100,alpha=0.2)
- train, test 데이터에 약간 고르게 분포
addr2
summary(transD$addr2) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
10.00 87.00 87.00 86.77 87.00 102.00 131315 round(NROW(which(is.na(transD$addr2)))/NROW(transD),2)[1] 0.12- NA가 131315개로 전체의 약 12%
str(as.factor(transD$addr2)) Factor w/ 93 levels "10","11","12",..: 78 78 78 78 78 78 78 78 78 78 ...- level이 무려 93개
ggplot(transD,aes(x=addr2,color=datatype)) + geom_histogram(position='dodge',bins=100,alpha=0.2)
- train, test 데이터에 약간 고르게 분포
P_emaildomain
str(transD$P_emaildomain) Factor w/ 60 levels "aim.com","anonymous.com",..: NA 17 36 54 17 17 54 30 2 54 ...round(NROW(which(is.na(transD$P_emaildomain)))/NROW(transD),2)[1] 0.15- NA가 163648개로 전체의 약 15%
- 이메일 주소에서 @ 뒷부분 인듯 -> 합칠 수 있을듯
- level이 60개
level 너무 많으므로 합치기
(tmp <- transD %>% group_by(P_emaildomain) %>%
summarise(n=n(),prop=round(n/NROW(transD),3)) %>% arrange(desc(prop)))# A tibble: 61 x 3
P_emaildomain n prop
<fct> <int> <dbl>
1 gmail.com 435803 0.397
2 yahoo.com 182784 0.167
3 <NA> 163648 0.149
4 hotmail.com 85649 0.078
5 anonymous.com 71062 0.065
6 aol.com 52337 0.048
7 comcast.net 14474 0.013
8 icloud.com 12316 0.011
9 outlook.com 9934 0.009
10 att.net 7647 0.007
# … with 51 more rows- 형태보니까 .으로 자르고 앞에꺼로 하면 될듯
transD$P_emaildomain <- as.character(transD$P_emaildomain)
transD$P_emaildomain <- stringr::str_split(string=transD$P_emaildomain, pattern='[.]', simplify=T)[,1]
sort(table(transD$P_emaildomain),decreasing=T)
gmail yahoo hotmail anonymous aol
436796 186568 87414 71062 52337
comcast icloud outlook att msn
14474 12316 10797 7647 7480
live sbcglobal verizon ymail bellsouth
7296 5767 5011 4075 3437
me cox optonline charter mail
2713 2657 1937 1443 1156
rocketmail earthlink mac netzero frontier
1105 979 862 706 594
roadrunner juno windstream web aim
583 574 552 518 468
embarqmail twc frontiernet centurylink q
464 439 397 386 362
suddenlink cfl cableone prodigy gmx
323 318 311 303 298
sc protonmail ptd servicios-ta scranton
277 159 140 80 2 - 여기서 ‘gmail’,‘yahoo’,‘hotmail’,‘anonymous’,‘etc’,NA 로(6 levels)
transD$P_emaildomain <- as.factor(ifelse(is.na(transD$P_emaildomain),NA,
ifelse(!(transD$P_emaildomain %in% c('gmail','yahoo','hotmail','anonymous')),
'etc',transD$P_emaildomain)))
round(with(transD,prop.table(table(P_emaildomain,datatype))),2) datatype
P_emaildomain train test
anonymous 0.04 0.04
etc 0.09 0.08
gmail 0.25 0.22
hotmail 0.05 0.04
yahoo 0.11 0.09- train, test 데이터에 고르게 분포
R_emaildomain
str(transD$R_emaildomain) Factor w/ 60 levels "aim.com","anonymous.com",..: NA NA NA NA NA NA NA NA NA NA ...round(NROW(which(is.na(transD$R_emaildomain)))/NROW(transD),2)[1] 0.75- NA가 163648개로 전체의 약 75%
- 이메일 주소에서 @ 뒷부분 인듯 -> 합칠 수 있을듯
- level이 P_emaildomain과 일치(60개)
P_emaildomian과 똑같은 level로 줄이기
transD$R_emaildomain <- as.character(transD$R_emaildomain)
transD$R_emaildomain <- stringr::str_split(string=transD$R_emaildomain, pattern='[.]', simplify=T)[,1]
transD$R_emaildomain <- as.factor(ifelse(is.na(transD$R_emaildomain),NA,
ifelse(!(transD$R_emaildomain %in% c('gmail','yahoo','hotmail','anonymous')),
'etc',transD$R_emaildomain)))
round(with(transD,prop.table(table(R_emaildomain,datatype))),2) datatype
R_emaildomain train test
anonymous 0.08 0.07
etc 0.06 0.06
gmail 0.21 0.23
hotmail 0.10 0.10
yahoo 0.05 0.04- train, test 데이터에 고르게 분포
M4
summary(transD$M4) M0 M1 M2 NA's
357789 97306 122947 519189 round(prop.table(table(ifelse(is.na(transD$M4),'missing',as.character(transD$M4)))),2)
M0 M1 M2 missing
0.33 0.09 0.11 0.47 - NA가 519189개로 전체의 약 0.47%
- M0이 다른 값에 비해 많음
round(prop.table(table(ifelse(is.na(transD$M4),'missing',as.character(transD$M4)),transD$datatype)),2)
train test
M0 0.18 0.15
M1 0.05 0.04
M2 0.05 0.06
missing 0.26 0.22- train, test 데이터에 고르게 분포
M1~3, M5~9
sapply(transD[,paste0('M',c(1:3,5:9))],summary) M1 M2 M3 M5 M6 M7
Mode "logical" "logical" "logical" "logical" "logical" "logical"
FALSE "56" "61169" "131248" "240155" "419433" "444604"
TRUE "649436" "588323" "518244" "196962" "349499" "71344"
NA's "447739" "447739" "447739" "660114" "328299" "581283"
M8 M9
Mode "logical" "logical"
FALSE "323650" "74040"
TRUE "192325" "441935"
NA's "581256" "581256" - 모두 T or F를 값으로 가짐
각 변수 값, NA 비율
sapply(transD[,paste0('M',c(1:3,5:9))],
function(x){round(prop.table(table(ifelse(is.na(x),'missing',x))),2)}) M1 M2 M3 M5 M6 M7 M8 M9
FALSE 0.00 0.06 0.12 0.22 0.38 0.41 0.29 0.07
missing 0.41 0.41 0.41 0.60 0.30 0.53 0.53 0.53
TRUE 0.59 0.54 0.47 0.18 0.32 0.07 0.18 0.40- 전체적으로 NA 비율이 모두 높은 편이고
- M1, M2, M3, M9은 F값이 적은 클래스 불균형
- M5, M6, M8은 클래스 균형…?
- M7은 T값이 적은 클래스 불균형
수치형(T->1, F->0)으로 변환
for(i in paste0('M',c(1:3,5:9)))
transD[,i] <- as.numeric(transD[,i])
for(i in paste0('M',c(1:3,5:9)))
print(round(prop.table(table(ifelse(is.na(transD[,i]),'missing',transD[,i]),transD$datatype)),2))
train test
0 0.00 0.00
1 0.29 0.30
missing 0.25 0.16
train test
0 0.03 0.02
1 0.26 0.28
missing 0.25 0.16
train test
0 0.06 0.06
1 0.23 0.24
missing 0.25 0.16
train test
0 0.12 0.10
1 0.10 0.08
missing 0.32 0.28
train test
0 0.21 0.17
1 0.18 0.14
missing 0.15 0.14
train test
0 0.19 0.21
1 0.03 0.04
missing 0.32 0.21
train test
0 0.14 0.15
1 0.08 0.09
missing 0.32 0.21
train test
0 0.04 0.03
1 0.19 0.22
missing 0.32 0.21- 모두 train, test 데이터에 고르게 분포(NA 비율은 고르지 X)
(2)-2. Numeric Features
TransactionAmt
summary(transD$TransactionAmt) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.02 42.00 67.95 134.89 125.00 31937.39 - NA는 없음
- 실수임
tapply(transD$TransactionAmt, transD$datatype, summary)$train
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.25 43.32 68.77 135.03 125.00 31937.39
$test
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.018 40.000 67.950 134.726 125.000 10270.000 wilcox.test(transD$TransactionAmt[transD$datatype=='train'],transD$TransactionAmt[transD$datatype=='test']) # p-value가 0.05보다 작으므로 차이 X
Wilcoxon rank sum test with continuity correction
data: transD$TransactionAmt[transD$datatype == "train"] and transD$TransactionAmt[transD$datatype == "test"]
W = 151430000000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0- train, test 데이터에 고르게 분포
dist1
summary(transD$dist1) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0 3.0 8.0 103.6 22.0 10286.0 643488 round(NROW(which(is.na(transD$dist1)))/NROW(transD),2)[1] 0.59- NA가 무려 59%…
- 정수임
tapply(transD$dist1, transD$datatype, summary)$train
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0 3.0 8.0 118.5 24.0 10286.0 352271
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 3.00 8.00 87.07 20.00 8081.00 291217 wilcox.test(transD$dist1[transD$datatype=='train'],transD$dist1[transD$datatype=='test']) # p-value가 0.05보다 작으므로 차이 X
Wilcoxon rank sum test with continuity correction
data: transD$dist1[transD$datatype == "train"] and transD$dist1[transD$datatype == "test"]
W = 26759000000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0- mean, 3Q, max에서 차이가 있다고 생각했지만 wilcox.test에서는 차이 X
dist2
summary(transD$dist2) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0 7.0 41.0 234.5 201.0 11623.0 1023168 round(NROW(which(is.na(transD$dist2)))/NROW(transD),2)[1] 0.93- NA가 무려 93%…
- 정수임
tapply(transD$dist2, transD$datatype, summary)$train
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0 7.0 37.0 231.9 206.0 11623.0 552913
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0 7.0 44.0 237.2 196.0 9213.0 470255 wilcox.test(transD$dist2[transD$datatype=='train'],transD$dist2[transD$datatype=='test']) # p-value가 0.05보다 작으므로 차이 X
Wilcoxon rank sum test with continuity correction
data: transD$dist2[transD$datatype == "train"] and transD$dist2[transD$datatype == "test"]
W = 671430000, p-value = 0.000001309
alternative hypothesis: true location shift is not equal to 0- median, mean, 3Q, max에서 차이가 있다고 생각했지만 wilcox.test에서는 차이 X
C1~14
sapply(transD[,paste0('C',1:14)],summary) C1 C2 C3 C4 C5
Min. 0.00000 0.00000 0.00000000 0.000000 0.000000
1st Qu. 1.00000 1.00000 0.00000000 0.000000 0.000000
Median 1.00000 1.00000 0.00000000 0.000000 0.000000
Mean 12.24565 13.16624 0.01569227 3.304229 5.290377
3rd Qu. 3.00000 3.00000 0.00000000 0.000000 1.000000
Max. 4685.00000 5691.00000 31.00000000 2253.000000 376.000000
NA's 3.00000 3.00000 3.00000000 3.000000 3.000000
C6 C7 C8 C9 C10
Min. 0.000000 0.000000 0.000000 0.000000 0.000000
1st Qu. 1.000000 0.000000 0.000000 0.000000 0.000000
Median 1.000000 0.000000 0.000000 1.000000 0.000000
Mean 8.047716 2.308044 3.643492 4.541059 3.656317
3rd Qu. 2.000000 0.000000 1.000000 2.000000 0.000000
Max. 2253.000000 2255.000000 3331.000000 572.000000 3257.000000
NA's 3.000000 3.000000 3.000000 3.000000 3.000000
C11 C12 C13 C14
Min. 0.000000 0.000000 0.00000 0.000000
1st Qu. 1.000000 0.000000 1.00000 1.000000
Median 1.000000 0.000000 3.00000 1.000000
Mean 8.968402 3.417374 30.36952 7.274049
3rd Qu. 2.000000 0.000000 13.00000 2.000000
Max. 3188.000000 3188.000000 2918.00000 1429.000000
NA's 3.000000 3.000000 4748.00000 3.000000- C13을 제외한 변수들은 모두 3개의 NA만 있음
- 모두 정수임
- 모두 max에 비해 median, mean이 작음…
for(i in paste0('C',1:14))
{
cat(paste0(i,' >>>\n'))
print(tapply(transD[,i],transD$datatype,summary))
print(wilcox.test(transD[transD$datatype=='train',i],transD[transD$datatype=='test',i]))
}C1 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 1.00 1.00 14.09 3.00 4685.00
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 1.00 1.00 10.09 3.00 2950.00 3
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 151930000000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0
C2 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 1.00 1.00 15.27 3.00 5691.00
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 1.00 1.00 10.71 3.00 3275.00 3
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 150510000000, p-value = 0.000000003455
alternative hypothesis: true location shift is not equal to 0
C3 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.000000 0.000000 0.005644 0.000000 26.000000
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0000 0.0000 0.0000 0.0274 0.0000 31.0000 3
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 146410000000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0
C4 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 0.000 0.000 4.092 0.000 2253.000
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 0.000 0.000 2.386 1.000 1601.000 3
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 144770000000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0
C5 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 0.000 0.000 5.572 1.000 349.000
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 0.000 0.000 4.963 1.000 376.000 3
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 154110000000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0
C6 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 1.000 1.000 9.071 2.000 2253.000
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 1.000 1.000 6.855 2.000 1601.000 3
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 150770000000, p-value = 0.000000000000004828
alternative hypothesis: true location shift is not equal to 0
C7 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 0.000 0.000 2.849 0.000 2255.000
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 0.000 0.000 1.678 0.000 1621.000 3
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 146340000000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0
C8 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 0.000 0.000 5.145 0.000 3331.000
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 0.000 0.000 1.894 1.000 1005.000 3
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 144790000000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0
C9 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 0.00 1.00 4.48 2.00 210.00
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 0.000 1.000 4.612 2.000 572.000 3
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 148810000000, p-value = 0.0000003803
alternative hypothesis: true location shift is not equal to 0
C10 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 0.00 0.00 5.24 0.00 3257.00
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.00 0.00 1.81 1.00 881.00 3
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 143820000000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0
C11 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 1.00 1.00 10.24 2.00 3188.00
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 1.000 1.000 7.485 2.000 2234.000 3
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 154340000000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0
C12 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 0.000 0.000 4.076 0.000 3188.000
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.00 0.00 2.65 1.00 2234.00 3
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 134170000000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0
C13 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 1.00 3.00 32.54 12.00 2918.00
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 1.00 3.00 27.82 13.00 1562.00 4748
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 147720000000, p-value = 0.002285
alternative hypothesis: true location shift is not equal to 0
C14 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 1.000 1.000 8.295 2.000 1429.000
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 1.000 1.000 6.084 2.000 797.000 3
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 153150000000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0- 모두 wilcox.test에 따르면 train, test 데이터의 분포 차이 X
D1~15
sapply(transD[,paste0('D',1:15)],summary) D1 D2 D3 D4 D5
Min. 0.0000 0.0000 0.00000 -122.0000 0.00000
1st Qu. 0.0000 26.0000 1.00000 0.0000 1.00000
Median 4.0000 104.0000 8.00000 23.0000 9.00000
Mean 100.7141 178.4939 30.77255 157.7009 46.66927
3rd Qu. 133.0000 290.0000 27.00000 272.0000 33.00000
Max. 641.0000 641.0000 1076.00000 1091.0000 1088.00000
NA's 7300.0000 515566.0000 466020.00000 245773.0000 534216.00000
D6 D7 D8 D9 D10
Min. -83.00000 0.00000 0.00000 0.0000000 0.0000
1st Qu. 0.00000 0.00000 1.00000 0.2083330 0.0000
Median 0.00000 0.00000 37.79166 0.6666660 13.0000
Mean 77.77125 53.88817 153.41719 0.5575328 141.5345
3rd Qu. 21.00000 18.00000 201.16667 0.8333330 224.0000
Max. 1091.00000 1088.00000 2029.58337 0.9583330 1091.0000
NA's 899261.00000 998181.00000 947967.00000 947967.0000000 88567.0000
D11 D12 D13 D14 D15
Min. -53.000 -83.00000 0.00000 -193.00000 -83.0000
1st Qu. 0.000 0.00000 0.00000 0.00000 0.0000
Median 69.000 0.00000 0.00000 0.00000 50.0000
Mean 183.577 66.11652 18.11744 58.00937 185.1521
3rd Qu. 333.000 18.00000 0.00000 0.00000 341.0000
Max. 883.000 879.00000 1066.00000 1085.00000 1091.0000
NA's 455805.000 963260.00000 911895.00000 919850.00000 101182.0000- 모두 정수임.
각 변수의 NA 비율
sapply(transD[,paste0('D',1:15)],function(x){round(NROW(which(is.na(x)))/NROW(x),2)}) D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12 D13 D14 D15
0.01 0.47 0.42 0.22 0.49 0.82 0.91 0.86 0.86 0.08 0.42 0.88 0.83 0.84 0.09 for(i in paste0('D',1:15))
{
cat(paste0(i,' >>>\n'))
print(tapply(transD[,i],transD$datatype,summary))
print(wilcox.test(transD[transD$datatype=='train',i],transD[transD$datatype=='test',i]))
}D1 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.00 3.00 94.35 122.00 640.00 1269
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0 0.0 5.0 108.2 148.0 641.0 6031
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 143020000000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0
D2 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0 26.0 97.0 169.6 276.0 640.0 280797
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0 26.0 112.0 188.7 305.0 641.0 234769
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 40471000000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0
D3 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 1.00 8.00 28.34 27.00 819.00 262878
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 1.00 7.00 33.39 28.00 1076.00 203142
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 50349000000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0
D4 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-122 0 26 140 253 869 168922
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0 0.0 21.0 175.1 290.0 1091.0 76851
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 88613000000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0
D5 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 1.00 10.00 42.34 32.00 819.00 309841
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 0.00 8.00 50.98 34.00 1088.00 224375
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 40283000000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0
D6 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-83.0 0.0 0.0 69.8 40.0 873.0 517353
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0 0.0 0.0 82.4 13.0 1091.0 381908
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 4713500000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0
D7 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0 0.0 0.0 41.6 17.0 843.0 551623
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0 0.0 0.0 61.8 18.0 1088.0 446558
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 1147100000, p-value = 0.00000001225
alternative hypothesis: true location shift is not equal to 0
D8 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0 1.0 37.9 146.1 188.0 1707.8 515614
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0 1.1 37.7 160.8 221.1 2029.6 432353
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 2745600000, p-value = 0.000002352
alternative hypothesis: true location shift is not equal to 0
D9 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0 0.2 0.7 0.6 0.8 1.0 515614
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0 0.2 0.7 0.6 0.8 1.0 432353
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 2825700000, p-value = 0.0000009422
alternative hypothesis: true location shift is not equal to 0
D10 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0 0 15 124 197 876 76022
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0 0.0 10.0 159.8 250.0 1091.0 12545
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 124790000000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0
D11 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-53.0 0.0 43.0 146.6 274.0 670.0 279287
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0 0.0 102.0 218.4 401.0 883.0 176518
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 43826000000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0
D12 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-83 0 0 54 13 648 525823
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0 0.0 0.0 77.4 25.0 879.0 437437
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 2082700000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0
D13 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0 0.0 0.0 17.9 0.0 847.0 528588
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0 0.0 0.0 18.2 0.0 1066.0 383307
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 3948800000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0
D14 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-193.0 0.0 0.0 57.7 2.0 878.0 528353
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0 0.0 0.0 58.2 0.0 1085.0 391497
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 3744500000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0
D15 >>>
$train
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-83.0 0.0 52.0 163.7 314.0 879.0 89113
$test
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0 0.0 48.0 206.9 370.0 1091.0 12069
Wilcoxon rank sum test with continuity correction
data: transD[transD$datatype == "train", i] and transD[transD$datatype == "test", i]
W = 119920000000, p-value < 0.00000000000000022
alternative hypothesis: true location shift is not equal to 0- 모두 wilcox.test에 따르면 train, test 데이터의 분포 차이 X
V1~339
Vsummary <- data.frame(t(sapply(transD[,paste0('V',1:339)],summary)))
colnames(Vsummary) <- c('min','1Q','median','mean','3Q','max','na_cnt')
Vsummary min 1Q median mean 3Q max na_cnt
V1 0 1 1 0.99997194 1.0000 1.00 455805
V2 0 1 1 1.04594139 1.0000 11.00 455805
V3 0 1 1 1.08289343 1.0000 11.00 455805
V4 0 1 1 0.84857022 1.0000 10.00 455805
V5 0 1 1 0.88095743 1.0000 10.00 455805
V6 0 1 1 1.04581666 1.0000 13.00 455805
V7 0 1 1 1.07613193 1.0000 13.00 455805
V8 0 1 1 1.02516892 1.0000 11.00 455805
V9 0 1 1 1.04067811 1.0000 11.00 455805
V10 0 0 0 0.46016064 1.0000 5.00 455805
V11 0 0 0 0.47635425 1.0000 7.00 455805
V12 0 0 1 0.56466142 1.0000 4.00 88662
V13 0 0 1 0.59978742 1.0000 6.00 88662
V14 0 1 1 0.99966983 1.0000 1.00 88662
V15 0 0 0 0.13146448 0.0000 13.00 88662
V16 0 0 0 0.13269791 0.0000 25.00 88662
V17 0 0 0 0.19563956 0.0000 15.00 88662
V18 0 0 0 0.19723489 0.0000 15.00 88662
V19 0 1 1 0.83847511 1.0000 13.00 88662
V20 0 1 1 0.86629274 1.0000 25.00 88662
V21 0 0 0 0.18852850 0.0000 5.00 88662
V22 0 0 0 0.19212865 0.0000 8.00 88662
V23 0 1 1 1.04003395 1.0000 15.00 88662
V24 0 1 1 1.06364165 1.0000 29.00 88662
V25 0 1 1 0.99090394 1.0000 13.00 88662
V26 0 1 1 1.00024788 1.0000 25.00 88662
V27 0 0 0 0.01149153 0.0000 7.00 88662
V28 0 0 0 0.01156292 0.0000 7.00 88662
V29 0 0 0 0.36302425 1.0000 5.00 88662
V30 0 0 0 0.37980842 1.0000 9.00 88662
V31 0 0 0 0.19699396 0.0000 13.00 88662
V32 0 0 0 0.19918320 0.0000 25.00 88662
V33 0 0 0 0.14259312 0.0000 13.00 88662
V34 0 0 0 0.15429881 0.0000 25.00 88662
V35 0 0 1 0.54337169 1.0000 4.00 245823
V36 0 0 1 0.57598472 1.0000 6.00 245823
V37 0 1 1 1.12316422 1.0000 54.00 245823
V38 0 1 1 1.18601423 1.0000 54.00 245823
V39 0 0 0 0.23011881 0.0000 30.00 245823
V40 0 0 0 0.24488494 0.0000 31.00 245823
V41 0 1 1 0.99957835 1.0000 1.00 245823
V42 0 0 0 0.21686900 0.0000 8.00 245823
V43 0 0 0 0.23413334 0.0000 11.00 245823
V44 0 1 1 1.09372592 1.0000 48.00 245823
V45 0 1 1 1.13377957 1.0000 69.00 245823
V46 0 1 1 1.02224785 1.0000 8.00 245823
V47 0 1 1 1.03819320 1.0000 12.00 245823
V48 0 0 0 0.35510237 1.0000 5.00 245823
V49 0 0 0 0.36843323 1.0000 7.00 245823
V50 0 0 0 0.21887391 0.0000 7.00 245823
V51 0 0 0 0.17835280 0.0000 8.00 245823
V52 0 0 0 0.19419127 0.0000 12.00 245823
V53 0 0 1 0.59083373 1.0000 5.00 89995
V54 0 0 1 0.62707846 1.0000 7.00 89995
V55 0 1 1 1.07991871 1.0000 49.00 89995
V56 0 1 1 1.13814339 1.0000 51.00 89995
V57 0 0 0 0.13685273 0.0000 6.00 89995
V58 0 0 0 0.14138990 0.0000 10.00 89995
V59 0 0 0 0.19912811 0.0000 16.00 89995
V60 0 0 0 0.21012752 0.0000 17.00 89995
V61 0 1 1 0.85439262 1.0000 6.00 89995
V62 0 1 1 0.88977062 1.0000 10.00 89995
V63 0 0 0 0.19254177 0.0000 8.00 89995
V64 0 0 0 0.20933227 0.0000 10.00 89995
V65 0 1 1 0.99978257 1.0000 1.00 89995
V66 0 1 1 0.99790317 1.0000 8.00 89995
V67 0 1 1 1.01458943 1.0000 10.00 89995
V68 0 0 0 0.01163084 0.0000 7.00 89995
V69 0 0 0 0.36528877 1.0000 5.00 89995
V70 0 0 0 0.38138927 1.0000 8.00 89995
V71 0 0 0 0.19853639 0.0000 6.00 89995
V72 0 0 0 0.20376952 0.0000 10.00 89995
V73 0 0 0 0.15088222 0.0000 8.00 89995
V74 0 0 0 0.16876482 0.0000 10.00 89995
V75 0 0 1 0.54159697 1.0000 5.00 101245
V76 0 0 1 0.58072302 1.0000 7.00 101245
V77 0 1 1 1.10530971 1.0000 80.00 101245
V78 0 1 1 1.16779252 1.0000 80.00 101245
V79 0 0 0 0.14379620 0.0000 7.00 101245
V80 0 0 0 0.20676094 0.0000 19.00 101245
V81 0 0 0 0.21860147 0.0000 19.00 101245
V82 0 1 1 0.85445177 1.0000 7.00 101245
V83 0 1 1 0.88926049 1.0000 7.00 101245
V84 0 0 0 0.19723068 0.0000 10.00 101245
V85 0 0 0 0.21420381 0.0000 10.00 101245
V86 0 1 1 1.07858042 1.0000 80.00 101245
V87 0 1 1 1.11548456 1.0000 80.00 101245
V88 0 1 1 0.99953614 1.0000 1.00 101245
V89 0 0 0 0.01176221 0.0000 7.00 101245
V90 0 0 0 0.37161466 1.0000 5.00 101245
V91 0 0 0 0.38885185 1.0000 8.00 101245
V92 0 0 0 0.20472175 0.0000 7.00 101245
V93 0 0 0 0.21037143 0.0000 7.00 101245
V94 0 0 0 0.14480123 0.0000 2.00 101245
V95 0 0 0 0.66555993 0.0000 880.00 314
V96 0 0 0 2.25288513 1.0000 1410.00 314
V97 0 0 0 1.17053615 0.0000 976.00 314
V98 0 0 0 0.05992796 0.0000 12.00 314
V99 0 0 0 0.91577120 1.0000 88.00 314
V100 0 0 0 0.27197500 0.0000 30.00 314
V101 0 0 0 0.52066747 0.0000 869.00 314
V102 0 0 0 1.09705566 0.0000 1285.00 314
V103 0 0 0 0.75539991 0.0000 928.00 314
V104 0 0 0 0.08361526 0.0000 59.00 314
V105 0 0 0 0.23832068 0.0000 99.00 314
V106 0 0 0 0.14166705 0.0000 80.00 314
V107 0 1 1 0.99977391 1.0000 1.00 314
V108 0 1 1 1.00570326 1.0000 8.00 314
V109 0 1 1 1.01756013 1.0000 8.00 314
V110 0 1 1 1.00912558 1.0000 8.00 314
V111 0 1 1 1.00331383 1.0000 9.00 314
V112 0 1 1 1.00651462 1.0000 9.00 314
V113 0 1 1 1.00422366 1.0000 9.00 314
V114 0 1 1 1.01128891 1.0000 9.00 314
V115 0 1 1 1.03738296 1.0000 9.00 314
V116 0 1 1 1.01837423 1.0000 9.00 314
V117 0 1 1 1.00064636 1.0000 3.00 314
V118 0 1 1 1.00185429 1.0000 3.00 314
V119 0 1 1 1.00106116 1.0000 3.00 314
V120 0 1 1 1.00130001 1.0000 4.00 314
V121 0 1 1 1.00527661 1.0000 4.00 314
V122 0 1 1 1.00232834 1.0000 4.00 314
V123 0 1 1 1.03411653 1.0000 13.00 314
V124 0 1 1 1.09904396 1.0000 13.00 314
V125 0 1 1 1.05300766 1.0000 13.00 314
V126 0 0 0 100.24666211 0.0000 519038.50 314
V127 0 0 0 276.20772543 108.0000 544500.00 314
V128 0 0 0 158.94894905 0.0000 519038.50 314
V129 0 0 0 9.64189138 0.0000 64800.00 314
V130 0 0 0 99.18746100 59.0000 167200.00 314
V131 0 0 0 33.12377197 0.0000 167200.00 314
V132 0 0 0 69.68222869 0.0000 519038.50 314
V133 0 0 0 136.54160005 0.0000 519038.50 314
V134 0 0 0 96.71366594 0.0000 519038.50 314
V135 0 0 0 20.47568022 0.0000 302500.00 314
V136 0 0 0 39.85202888 0.0000 302500.00 314
V137 0 0 0 28.56454969 0.0000 302500.00 314
V138 0 0 0 0.03732327 0.0000 22.00 939501
V139 0 0 1 1.05918341 1.0000 33.00 939501
V140 0 0 1 1.12576555 1.0000 59.00 939501
V141 0 0 0 0.04056933 0.0000 6.00 939501
V142 0 0 0 0.05544285 0.0000 11.00 939501
V143 0 0 0 5.21177044 0.0000 869.00 939225
V144 0 0 0 3.27579965 0.0000 85.00 939225
V145 0 0 0 19.70028986 0.0000 297.00 939225
V146 0 0 0 0.14922970 0.0000 24.00 939501
V147 0 0 0 0.16590376 0.0000 26.00 939501
V148 0 0 1 0.75987447 1.0000 20.00 939501
V149 0 0 1 0.76830026 1.0000 20.00 939501
V150 1 1 1 208.99880384 1.0000 3389.00 939225
V151 1 1 1 5.97028594 1.0000 58.00 939225
V152 1 1 1 8.90347835 1.0000 69.00 939225
V153 0 0 1 0.75105560 1.0000 18.00 939501
V154 0 0 1 0.75536677 1.0000 18.00 939501
V155 0 0 1 0.76204907 1.0000 24.00 939501
V156 0 0 1 0.77053192 1.0000 24.00 939501
V157 0 0 1 0.80972548 1.0000 24.00 939501
V158 0 0 1 0.82435808 1.0000 24.00 939501
V159 0 0 0 4399.09931228 0.0000 284129.81 939225
V160 0 0 0 71815.29447248 0.0000 3867868.50 939225
V161 0 0 0 4.91595733 0.0000 3300.00 939501
V162 0 0 0 7.39617669 0.0000 4000.00 939501
V163 0 0 0 5.81369397 0.0000 4000.00 939501
V164 0 0 0 954.90558708 0.0000 928882.00 939225
V165 0 0 0 3782.61766672 0.0000 928882.00 939225
V166 0 0 0 854.37538933 0.0000 453750.00 939225
V167 0 0 0 2.46515659 0.0000 872.00 820866
V168 0 0 0 3.66408192 1.0000 964.00 820866
V169 0 0 0 0.18515247 0.0000 39.00 821037
V170 0 1 1 1.46181669 1.0000 63.00 821037
V171 0 1 1 1.71268746 1.0000 68.00 821037
V172 0 0 0 0.13048686 0.0000 37.00 820866
V173 0 0 0 0.05776419 0.0000 9.00 820866
V174 0 0 0 0.13699791 0.0000 8.00 821037
V175 0 0 0 0.20584806 0.0000 22.00 821037
V176 0 1 1 1.50550902 1.0000 239.00 820866
V177 0 0 0 2.10101496 0.0000 861.00 820866
V178 0 0 0 3.89580808 0.0000 1235.00 820866
V179 0 0 0 2.86720460 0.0000 920.00 820866
V180 0 0 0 0.69776317 0.0000 179.00 821037
V181 0 0 0 0.21862030 0.0000 85.00 820866
V182 0 0 0 0.63430608 0.0000 125.00 820866
V183 0 0 0 0.37615110 0.0000 106.00 820866
V184 0 0 0 0.13296813 0.0000 16.00 821037
V185 0 0 0 0.16468497 0.0000 31.00 821037
V186 0 1 1 1.14277857 1.0000 39.00 820866
V187 0 1 1 1.64972410 1.0000 218.00 820866
V188 0 1 1 1.00491321 1.0000 44.00 821037
V189 0 1 1 1.05332846 1.0000 44.00 821037
V190 0 1 1 1.31156261 1.0000 224.00 820866
V191 0 1 1 1.05193856 1.0000 21.00 820866
V192 0 1 1 1.18584119 1.0000 44.00 820866
V193 0 1 1 1.12168690 1.0000 37.00 820866
V194 0 1 1 0.93941577 1.0000 7.00 821037
V195 0 1 1 0.96185652 1.0000 18.00 821037
V196 0 1 1 1.07367431 1.0000 41.00 820866
V197 0 1 1 0.94180178 1.0000 14.00 821037
V198 0 1 1 0.97529273 1.0000 29.00 821037
V199 0 1 1 1.36773832 1.0000 224.00 820866
V200 0 1 1 1.11021963 1.0000 47.00 821037
V201 0 1 1 1.18386714 1.0000 55.00 821037
V202 0 0 0 337.05420164 0.0000 1065496.50 820866
V203 0 0 0 770.74506554 31.1232 1065496.50 820866
V204 0 0 0 503.08812354 19.7966 1065496.50 820866
V205 0 0 0 19.76879061 0.0000 64800.00 820866
V206 0 0 0 7.68558491 0.0000 64800.00 820866
V207 0 0 0 59.74327241 0.0000 167200.00 820866
V208 0 0 0 8.95878147 0.0000 4000.00 821037
V209 0 0 0 31.54558836 0.0000 10000.00 821037
V210 0 0 0 14.63852890 0.0000 4000.00 821037
V211 0 0 0 263.00952730 0.0000 928882.00 820866
V212 0 0 0 504.51760203 0.0000 958320.00 820866
V213 0 0 0 352.65557480 0.0000 928882.00 820866
V214 0 0 0 51.01520000 0.0000 453750.00 820866
V215 0 0 0 135.41410917 0.0000 756250.00 820866
V216 0 0 0 84.52528216 0.0000 756250.00 820866
V217 0 0 0 1.02140318 1.0000 303.00 840073
V218 0 0 0 1.68000995 1.0000 400.00 840073
V219 0 0 0 1.33839118 1.0000 378.00 840073
V220 0 0 0 0.18634387 0.0000 47.00 818499
V221 0 1 1 1.28041631 1.0000 384.00 818499
V222 0 1 1 1.55605384 1.0000 384.00 818499
V223 0 0 0 0.09451777 0.0000 16.00 840073
V224 0 0 0 0.35153874 0.0000 144.00 840073
V225 0 0 0 0.17838450 0.0000 58.00 840073
V226 0 0 0 0.25719208 0.0000 870.00 840073
V227 0 0 0 0.14564528 0.0000 360.00 818499
V228 0 1 1 1.49870508 1.0000 239.00 840073
V229 0 1 1 1.84706678 1.0000 262.00 840073
V230 0 1 1 1.66900894 1.0000 262.00 840073
V231 0 0 0 0.74274570 0.0000 293.00 840073
V232 0 0 0 1.04272082 0.0000 337.00 840073
V233 0 0 0 0.92595992 0.0000 332.00 840073
V234 0 0 0 2.59539629 0.0000 322.00 818499
V235 0 0 0 0.17069273 0.0000 23.00 840073
V236 0 0 0 0.26759424 0.0000 57.00 840073
V237 0 0 0 0.21906765 0.0000 39.00 840073
V238 0 0 0 0.12764232 0.0000 23.00 818499
V239 0 0 0 0.13711737 0.0000 23.00 818499
V240 0 1 1 1.00107716 1.0000 7.00 840073
V241 0 1 1 1.00031887 1.0000 5.00 840073
V242 0 1 1 1.11424494 1.0000 27.00 840073
V243 0 1 1 1.17981163 1.0000 57.00 840073
V244 0 1 1 1.11881800 1.0000 33.00 840073
V245 0 1 1 0.85377710 1.0000 262.00 818499
V246 0 1 1 1.29739304 1.0000 224.00 840073
V247 0 1 1 1.02161706 1.0000 18.00 840073
V248 0 1 1 1.06394123 1.0000 36.00 840073
V249 0 1 1 1.03989376 1.0000 22.00 840073
V250 0 1 1 0.77388674 1.0000 18.00 818499
V251 0 1 1 0.78447756 1.0000 18.00 818499
V252 0 1 1 1.02684342 1.0000 24.00 840073
V253 0 1 1 1.14014730 1.0000 163.00 840073
V254 0 1 1 1.06409678 1.0000 60.00 840073
V255 0 1 1 0.78766342 1.0000 87.00 818499
V256 0 1 1 0.80319088 1.0000 87.00 818499
V257 0 1 1 1.36624177 1.0000 224.00 840073
V258 0 1 1 1.50333258 1.0000 269.00 840073
V259 0 1 1 0.94969002 1.0000 285.00 818499
V260 0 1 1 0.96502539 1.0000 14.00 840073
V261 0 1 1 1.11136344 1.0000 89.00 840073
V262 0 1 1 1.01703233 1.0000 34.00 840073
V263 0 0 0 189.18284225 5.0000 1065496.50 840073
V264 0 0 0 294.83873764 34.2582 1065496.50 840073
V265 0 0 0 239.44082686 21.5180 1065496.50 840073
V266 0 0 0 13.76758349 0.0000 64800.00 840073
V267 0 0 0 37.12428694 0.0000 64800.00 840073
V268 0 0 0 22.11907106 0.0000 64800.00 840073
V269 0 0 0 10.33737508 0.0000 64800.00 840073
V270 0 0 0 7.46451432 0.0000 10791.57 818499
V271 0 0 0 11.03503199 0.0000 10791.57 818499
V272 0 0 0 8.26565927 0.0000 10791.57 818499
V273 0 0 0 121.16136196 0.0000 928882.00 840073
V274 0 0 0 168.46090934 0.0000 928882.00 840073
V275 0 0 0 142.41296423 0.0000 928882.00 840073
V276 0 0 0 50.48022957 0.0000 453750.00 840073
V277 0 0 0 83.28597245 0.0000 756250.00 840073
V278 0 0 0 70.51225668 0.0000 756250.00 840073
V279 0 0 0 0.76078821 0.0000 880.00 15
V280 0 0 0 1.40241028 1.0000 975.00 15
V281 0 0 0 0.08994606 0.0000 30.00 7300
V282 0 0 1 0.82098316 1.0000 63.00 7300
V283 0 0 1 0.99869350 1.0000 68.00 7300
V284 0 0 0 0.08787787 0.0000 12.00 15
V285 0 0 0 1.15992476 1.0000 95.00 15
V286 0 0 0 0.03109324 0.0000 8.00 15
V287 0 0 0 0.35636830 0.0000 31.00 15
V288 0 0 0 0.18569249 0.0000 10.00 7300
V289 0 0 0 0.23816462 0.0000 12.00 7300
V290 1 1 1 1.11084144 1.0000 67.00 15
V291 1 1 1 1.54218586 1.0000 1055.00 15
V292 1 1 1 1.22680767 1.0000 323.00 15
V293 0 0 0 0.57978374 0.0000 869.00 15
V294 0 0 0 1.45598405 0.0000 1286.00 15
V295 0 0 0 0.89088748 0.0000 928.00 15
V296 0 0 0 0.28904766 0.0000 179.00 7300
V297 0 0 0 0.08976719 0.0000 85.00 15
V298 0 0 0 0.25395455 0.0000 125.00 15
V299 0 0 0 0.15122091 0.0000 106.00 15
V300 0 0 0 0.05064174 0.0000 14.00 7300
V301 0 0 0 0.05840003 0.0000 14.00 7300
V302 0 0 0 0.26792446 1.0000 16.00 15
V303 0 0 0 0.30101548 1.0000 20.00 15
V304 0 0 0 0.28145051 1.0000 17.00 15
V305 1 1 1 1.00000456 1.0000 2.00 15
V306 0 0 0 113.90411989 0.0000 718740.00 15
V307 0 0 0 339.73162834 154.0000 958320.00 15
V308 0 0 0 187.25215443 39.0000 718740.00 15
V309 0 0 0 11.84701292 0.0000 64800.00 15
V310 0 0 0 122.35314227 107.9500 167200.00 15
V311 0 0 0 4.64162221 0.0000 64800.00 15
V312 0 0 0 40.81750769 0.0000 167200.00 15
V313 0 0 0 21.29941186 0.0000 4817.47 7300
V314 0 0 0 43.29636724 0.0000 7539.75 7300
V315 0 0 0 26.77181409 0.0000 4817.47 7300
V316 0 0 0 78.53630065 0.0000 718740.00 15
V317 0 0 0 171.96558609 0.0000 958320.00 15
V318 0 0 0 113.17429270 0.0000 718740.00 15
V319 0 0 0 22.64300100 0.0000 453750.00 15
V320 0 0 0 44.27242623 0.0000 605000.00 15
V321 0 0 0 32.22686455 0.0000 605000.00 15
V322 0 0 0 3.43630260 0.0000 880.00 938449
V323 0 0 0 7.38158607 1.0000 1411.00 938449
V324 0 0 0 5.09735360 0.0000 976.00 938449
V325 0 0 0 0.05340656 0.0000 12.00 938449
V326 0 0 0 0.67371616 0.0000 44.00 938449
V327 0 0 0 0.23243819 0.0000 31.00 938449
V328 0 0 0 0.28080639 0.0000 85.00 938449
V329 0 0 0 0.90259601 0.0000 125.00 938449
V330 0 0 0 0.55157386 0.0000 106.00 938449
V331 0 0 0 526.01529668 0.0000 1040657.50 938449
V332 0 0 0 925.56012837 20.0000 1040657.50 938449
V333 0 0 0 709.67249108 0.0000 1040657.50 938449
V334 0 0 0 16.97973920 0.0000 64800.00 938449
V335 0 0 0 58.76738988 0.0000 64800.00 938449
V336 0 0 0 31.87862352 0.0000 64800.00 938449
V337 0 0 0 76.39082113 0.0000 375000.00 938449
V338 0 0 0 153.28699469 0.0000 612500.00 938449
V339 0 0 0 119.52863745 0.0000 612500.00 938449- 너무 많아서 보기 힘든데…
NA’s(NA 개수)로 그룹(15개) 짓기
table(Vsummary[,"na_cnt"])
15 314 7300 88662 89995 101245 245823 455805 818499 820866
32 43 11 23 22 20 18 11 16 31
821037 840073 938449 939225 939501
19 46 18 11 18 na_cnts <- as.numeric(names(table(Vsummary$na_cnt)))
V <- list()
for(i in 1:NROW(na_cnts))
V[[as.character(na_cnts[i])]] <- rownames(Vsummary[Vsummary$na_cnt==na_cnts[i],])for(i in paste0('C',1:14))
{
cat(paste0(i,' >>>\n'))
print(tapply(transD[,i],transD$datatype,summary))
print(wilcox.test(transD[transD$datatype=='train',i],transD[transD$datatype=='test',i]))
}- 모두 wilcox.test에 따르면 train, test 데이터의 분포 차이 X
3. Convert all to numeric features
(1) identityD
str(identityD)make dummy variables
factor_features <- setdiff(names(which(!sapply(identityD,is.numeric))),'datatype')
dummies <- dummyVars(as.formula(paste0('~',paste0(factor_features,collapse = '+'))),
data=identityD)
dummies <- predict(dummies, identityD)
identityD_ <- identityD
identityD_[,factor_features] <- NULL
identityD_ <- cbind(identityD_,dummies)
str(identityD_)(2) transD
str(transD)make dummy variables
factor_features <- setdiff(names(which(!sapply(transD,is.numeric))),'datatype')
dummies <- dummyVars(as.formula(paste0('~',paste0(factor_features,collapse = '+'))),
data=transD)
dummies <- predict(dummies, transD)
transD_ <- transD
transD_[,factor_features] <- NULL
transD_ <- cbind(transD_,dummies)
str(transD_)4. Modeling trial 1
# 데이터 준비
trainD <- transD_[transD_$datatype=='train',]
trainD$datatype <- NULL
testD <- transD_[transD_$datatype=='test',]
testD$datatype <- NULL
library(xgboost)
library(parallel)
library(doParallel)
train_matrix <- xgb.DMatrix(as.matrix(select(trainD,-'isFraud')), label=trainD$isFraud)
param <- list(max_depth=3, eta=0.5, silent=1, nrounds =10, objective="binary:logistic",
eval_metric="auc")
bst.cv <- xgb.cv(param=param, train_matrix, nfold=5, nrounds=10)
xgb_model <- xgb.train(param, train_matrix, nrounds=5)
xgb_model
imp_mat <- xgb.importance(feature_names=dimnames(as.matrix(select(trainD,-'isFraud')))[[2]],
model=xgb_model)
xgb.plot.importance(imp_mat, main = "Gain by Feature")
library(InformationValue)
pred <- predict(xgb_model, as.matrix(select(trainD,-'isFraud')))
optimalCutoff(trainD$isFraud, pred)
testD_mat <- as.matrix(select(testD,-'isFraud'))
testD_mat_pred <- predict(xgb_model, testD_mat)
submit1 <- data.frame(TransactionID=testD$TransactionID,isFraud=testD_mat_pred)
write.csv(submit1,"submit1.csv",row.names = F)
#y.test <- ifelse(pima.test$type == "Yes", 1, 0)
#confusionMatrix(y.test, xgb.pima.test, threshold = 0.48)
#1-misClassError(y.test, xgb.pima.test, threshold = 0.48)
## optimalCutoff() 함수 -> InformationValue 패키지에서 제공
## 오차를 최소화하기 위한 최적 확률 분계점(threshold) 를 제공한다.
#plotROC(y.test , xgb.pima.test)
# training 에 필요한 변수
ctrl <- trainControl(method='cv',number=5,verboseIter=T,classProbs=T) # 5fold CV
m <- "auc" # 모델 비교 기준
preprocess <- c("center","scale") # 전처리
cores <- detectCores() # 4 : 장착된 코어 수
seed <- 777
# nrounds : 최대 반복 횟수 (최종 모형에서의 트리 수)
# colsample_bytree : 트리를 생성할 때 표본 추출할 피처 수(비율로 표시됨 // 기본값은 1 [피처수의 100%])
# min_child_weight : 부스트되는 트리에서 최소 가중값. (기본값은 1)
# eta : 학습 속도. 해법에 관한 각 트리의 기여도를 의미. 기본값은 0.3
# gamma : 트리에서 다른 리프(leaf) 분할을 하기 위해 필요한 최소 손실 감소(minimum loss reduction)
# subsample : 데이터 관찰값의 비율. (기본값은 1[100%])
# max_depth : 개별 트리의 최대 깊이.
mygrid = expand.grid(nrounds = c(50, 70),
colsample_bytree=1,
min_child_weight=1,
eta = c(0.01, 0.1, 0.3), #0.3 기본값
gamma = c(0.5, 0.25),
subsample = 0.5,
max_depth = c(2, 3))
cl <- makeCluster(cores-1)
registerDoParallel(cl)
set.seed(seed)
xgb.fit = train(x=select(trainD,-'isFraud'), y=trainD$isFraud,
trControl=ctrl,
#uneGrid = grid,
method="xgbTree")5. NA 처리
(1) identiyD
1.
변수들의 NA 비율
# sort(round(colSums(is.na(identityD))/NROW(identityD),3),decreasing=T) # 개수
no_na_features <- c('TransactionID', 'id_01', 'id_12', 'datatype') # na 없는 변수들
(na_prop <- na_prop_func(identityD, no_na_features)) id_24 id_07 id_08 id_21 id_22 id_23
0.967 0.964 0.964 0.964 0.964 0.964
id_25 id_26 id_27 id_18 id_03 id_04
0.964 0.964 0.964 0.665 0.536 0.536
id_33_1 id_33_2 id_30 id_32 id_09 id_10
0.497 0.497 0.482 0.482 0.478 0.478
id_29 id_34 id_14 DeviceInfo id_16 id_13
0.478 0.476 0.471 0.183 0.109 0.100
id_05 id_06 id_20 id_17 id_19 id_31
0.051 0.051 0.039 0.038 0.038 0.032
id_02 id_11 id_15 id_28 id_35 id_36
0.029 0.029 0.029 0.029 0.029 0.029
id_37 id_38 DeviceType
0.029 0.029 0.000 - NA 비율 95% 이상인 변수 : id_24, id_07(num), id_08(num), id_21, id_22, id_23, id_25, id_26, id_27
변수들간 NA 여부 상관관계
(na_cor <- na_cor_func(identityD, no_na_features))- NA 상관관계에서도 지들끼리만 상관계수가 거의 1임
- 걍 빼고 하자
much_na_features <- c('id_24','id_07','id_08','id_21','id_22','id_23','id_25','id_26','id_27')
identityD <- select_(identityD, .dots='-much_na_features')2.
na_prop <- na_prop_func(identityD, no_na_features) # 비율
na_prop id_24 id_07 id_08 id_21 id_22 id_23
0.967 0.964 0.964 0.964 0.964 0.964
id_25 id_26 id_27 id_18 id_03 id_04
0.964 0.964 0.964 0.665 0.536 0.536
id_33_1 id_33_2 id_30 id_32 id_09 id_10
0.497 0.497 0.482 0.482 0.478 0.478
id_29 id_34 id_14 DeviceInfo id_16 id_13
0.478 0.476 0.471 0.183 0.109 0.100
id_05 id_06 id_20 id_17 id_19 id_31
0.051 0.051 0.039 0.038 0.038 0.032
id_02 id_11 id_15 id_28 id_35 id_36
0.029 0.029 0.029 0.029 0.029 0.029
id_37 id_38 DeviceType
0.029 0.029 0.000 - na_prop 에서 NA가 그 다음으로 많은 id_18
변수 간 ’NA의 존재 여부’의 상관관계
na_cor <- na_cor_func(identityD, no_na_features)
na_cor id_02 id_03 id_04 id_05 id_06 id_07 id_08 id_09 id_10
id_02 1.000 0.161 0.161 0.747 0.747 0.033 0.033 0.180 0.180
id_03 0.161 1.000 1.000 0.142 0.142 0.004 0.004 0.890 0.890
id_04 0.161 1.000 1.000 0.142 0.142 0.004 0.004 0.890 0.890
id_05 0.747 0.142 0.142 1.000 1.000 -0.139 -0.139 0.154 0.154
id_06 0.747 0.142 0.142 1.000 1.000 -0.139 -0.139 0.154 0.154
id_07 0.033 0.004 0.004 -0.139 -0.139 1.000 1.000 0.013 0.013
id_08 0.033 0.004 0.004 -0.139 -0.139 1.000 1.000 0.013 0.013
id_09 0.180 0.890 0.890 0.154 0.154 0.013 0.013 1.000 1.000
id_10 0.180 0.890 0.890 0.154 0.154 0.013 0.013 1.000 1.000
id_11 0.980 0.162 0.162 0.735 0.735 0.033 0.033 0.181 0.181
id_13 -0.057 -0.075 -0.075 -0.055 -0.055 0.064 0.064 -0.064 -0.064
id_14 0.183 0.053 0.053 0.114 0.114 0.152 0.152 0.088 0.088
id_15 0.993 0.160 0.160 0.742 0.742 0.033 0.033 0.179 0.179
id_16 0.495 0.325 0.325 0.369 0.369 0.055 0.055 0.313 0.313
id_17 0.872 0.131 0.131 0.854 0.854 -0.169 -0.169 0.138 0.138
id_18 0.123 -0.003 -0.003 0.164 0.164 0.143 0.143 0.014 0.014
id_19 0.867 0.130 0.130 0.849 0.849 -0.168 -0.168 0.137 0.137
id_20 0.854 0.127 0.127 0.836 0.836 -0.165 -0.165 0.133 0.133
id_21 0.033 0.004 0.004 -0.140 -0.140 0.998 0.998 0.013 0.013
id_22 0.033 0.005 0.005 -0.140 -0.140 0.999 0.999 0.013 0.013
id_23 0.033 0.005 0.005 -0.140 -0.140 0.999 0.999 0.013 0.013
id_24 0.032 0.002 0.002 -0.125 -0.125 0.962 0.962 0.009 0.009
id_25 0.033 0.004 0.004 -0.138 -0.138 0.996 0.996 0.013 0.013
id_26 0.033 0.005 0.005 -0.140 -0.140 0.998 0.998 0.013 0.013
id_27 0.033 0.005 0.005 -0.140 -0.140 0.999 0.999 0.013 0.013
id_28 0.980 0.162 0.162 0.735 0.735 0.033 0.033 0.181 0.181
id_29 0.180 0.890 0.890 0.154 0.154 0.013 0.013 1.000 1.000
id_30 0.179 0.048 0.048 0.113 0.113 0.148 0.148 0.085 0.085
id_31 0.946 0.146 0.146 0.743 0.743 0.034 0.034 0.166 0.166
id_32 0.179 0.048 0.048 0.113 0.113 0.148 0.148 0.085 0.085
id_34 0.177 0.047 0.047 0.111 0.111 0.135 0.135 0.082 0.082
id_35 0.993 0.160 0.160 0.742 0.742 0.033 0.033 0.179 0.179
id_36 0.993 0.160 0.160 0.742 0.742 0.033 0.033 0.179 0.179
id_37 0.993 0.160 0.160 0.742 0.742 0.033 0.033 0.179 0.179
id_38 0.993 0.160 0.160 0.742 0.742 0.033 0.033 0.179 0.179
DeviceType NA NA NA NA NA NA NA NA NA
DeviceInfo 0.365 0.172 0.172 0.272 0.272 0.040 0.040 0.191 0.191
id_33_1 0.174 0.042 0.042 0.112 0.112 0.145 0.145 0.078 0.078
id_33_2 0.174 0.042 0.042 0.112 0.112 0.145 0.145 0.078 0.078
id_11 id_13 id_14 id_15 id_16 id_17 id_18 id_19 id_20
id_02 0.980 -0.057 0.183 0.993 0.495 0.872 0.123 0.867 0.854
id_03 0.162 -0.075 0.053 0.160 0.325 0.131 -0.003 0.130 0.127
id_04 0.162 -0.075 0.053 0.160 0.325 0.131 -0.003 0.130 0.127
id_05 0.735 -0.055 0.114 0.742 0.369 0.854 0.164 0.849 0.836
id_06 0.735 -0.055 0.114 0.742 0.369 0.854 0.164 0.849 0.836
id_07 0.033 0.064 0.152 0.033 0.055 -0.169 0.143 -0.168 -0.165
id_08 0.033 0.064 0.152 0.033 0.055 -0.169 0.143 -0.168 -0.165
id_09 0.181 -0.064 0.088 0.179 0.313 0.138 0.014 0.137 0.133
id_10 0.181 -0.064 0.088 0.179 0.313 0.138 0.014 0.137 0.133
id_11 1.000 -0.058 0.184 0.987 0.498 0.858 0.120 0.854 0.841
id_13 -0.058 1.000 -0.260 -0.057 -0.116 -0.066 -0.018 -0.066 -0.066
id_14 0.184 -0.260 1.000 0.182 0.341 0.146 0.129 0.145 0.146
id_15 0.987 -0.057 0.182 1.000 0.492 0.866 0.122 0.861 0.848
id_16 0.498 -0.116 0.341 0.492 1.000 0.428 0.039 0.426 0.419
id_17 0.858 -0.066 0.146 0.866 0.428 1.000 0.141 0.995 0.979
id_18 0.120 -0.018 0.129 0.122 0.039 0.141 1.000 0.139 0.134
id_19 0.854 -0.066 0.145 0.861 0.426 0.995 0.139 1.000 0.975
id_20 0.841 -0.066 0.146 0.848 0.419 0.979 0.134 0.975 1.000
id_21 0.033 0.064 0.152 0.033 0.055 -0.169 0.142 -0.169 -0.165
id_22 0.033 0.064 0.153 0.033 0.055 -0.169 0.142 -0.169 -0.165
id_23 0.033 0.064 0.153 0.033 0.055 -0.169 0.142 -0.169 -0.165
id_24 0.032 0.062 0.153 0.032 0.054 -0.153 0.147 -0.153 -0.150
id_25 0.033 0.064 0.153 0.033 0.055 -0.167 0.143 -0.166 -0.163
id_26 0.033 0.064 0.153 0.033 0.055 -0.169 0.142 -0.169 -0.165
id_27 0.033 0.064 0.153 0.033 0.055 -0.169 0.142 -0.169 -0.165
id_28 1.000 -0.058 0.184 0.987 0.498 0.858 0.120 0.854 0.841
id_29 0.181 -0.064 0.088 0.179 0.313 0.138 0.014 0.137 0.133
id_30 0.180 -0.260 0.978 0.178 0.333 0.143 0.135 0.143 0.143
id_31 0.951 -0.059 0.182 0.939 0.478 0.867 0.120 0.863 0.849
id_32 0.180 -0.260 0.978 0.178 0.334 0.143 0.136 0.143 0.143
id_34 0.176 -0.263 0.952 0.180 0.325 0.141 0.135 0.140 0.141
id_35 0.987 -0.057 0.182 1.000 0.492 0.866 0.122 0.861 0.848
id_36 0.987 -0.057 0.182 1.000 0.492 0.866 0.122 0.861 0.848
id_37 0.987 -0.057 0.182 1.000 0.492 0.866 0.122 0.861 0.848
id_38 0.987 -0.057 0.182 1.000 0.492 0.866 0.122 0.861 0.848
DeviceType NA NA NA NA NA NA NA NA NA
DeviceInfo 0.367 -0.066 0.408 0.362 0.633 0.321 0.078 0.319 0.314
id_33_1 0.175 -0.242 0.949 0.173 0.324 0.140 0.133 0.140 0.140
id_33_2 0.175 -0.242 0.949 0.173 0.324 0.140 0.133 0.140 0.140
id_21 id_22 id_23 id_24 id_25 id_26 id_27 id_28 id_29
id_02 0.033 0.033 0.033 0.032 0.033 0.033 0.033 0.980 0.180
id_03 0.004 0.005 0.005 0.002 0.004 0.005 0.005 0.162 0.890
id_04 0.004 0.005 0.005 0.002 0.004 0.005 0.005 0.162 0.890
id_05 -0.140 -0.140 -0.140 -0.125 -0.138 -0.140 -0.140 0.735 0.154
id_06 -0.140 -0.140 -0.140 -0.125 -0.138 -0.140 -0.140 0.735 0.154
id_07 0.998 0.999 0.999 0.962 0.996 0.998 0.999 0.033 0.013
id_08 0.998 0.999 0.999 0.962 0.996 0.998 0.999 0.033 0.013
id_09 0.013 0.013 0.013 0.009 0.013 0.013 0.013 0.181 1.000
id_10 0.013 0.013 0.013 0.009 0.013 0.013 0.013 0.181 1.000
id_11 0.033 0.033 0.033 0.032 0.033 0.033 0.033 1.000 0.181
id_13 0.064 0.064 0.064 0.062 0.064 0.064 0.064 -0.058 -0.064
id_14 0.152 0.153 0.153 0.153 0.153 0.153 0.153 0.184 0.088
id_15 0.033 0.033 0.033 0.032 0.033 0.033 0.033 0.987 0.179
id_16 0.055 0.055 0.055 0.054 0.055 0.055 0.055 0.498 0.313
id_17 -0.169 -0.169 -0.169 -0.153 -0.167 -0.169 -0.169 0.858 0.138
id_18 0.142 0.142 0.142 0.147 0.143 0.142 0.142 0.120 0.014
id_19 -0.169 -0.169 -0.169 -0.153 -0.166 -0.169 -0.169 0.854 0.137
id_20 -0.165 -0.165 -0.165 -0.150 -0.163 -0.165 -0.165 0.841 0.133
id_21 1.000 0.999 0.999 0.962 0.996 0.998 0.999 0.033 0.013
id_22 0.999 1.000 1.000 0.962 0.997 0.999 1.000 0.033 0.013
id_23 0.999 1.000 1.000 0.962 0.997 0.999 1.000 0.033 0.013
id_24 0.962 0.962 0.962 1.000 0.958 0.961 0.962 0.032 0.009
id_25 0.996 0.997 0.997 0.958 1.000 0.996 0.997 0.033 0.013
id_26 0.998 0.999 0.999 0.961 0.996 1.000 0.999 0.033 0.013
id_27 0.999 1.000 1.000 0.962 0.997 0.999 1.000 0.033 0.013
id_28 0.033 0.033 0.033 0.032 0.033 0.033 0.033 1.000 0.181
id_29 0.013 0.013 0.013 0.009 0.013 0.013 0.013 0.181 1.000
id_30 0.148 0.149 0.149 0.153 0.149 0.148 0.149 0.180 0.085
id_31 0.034 0.035 0.035 0.033 0.034 0.034 0.035 0.951 0.166
id_32 0.148 0.149 0.149 0.153 0.149 0.148 0.149 0.180 0.085
id_34 0.135 0.135 0.135 0.140 0.136 0.135 0.135 0.176 0.082
id_35 0.033 0.033 0.033 0.032 0.033 0.033 0.033 0.987 0.179
id_36 0.033 0.033 0.033 0.032 0.033 0.033 0.033 0.987 0.179
id_37 0.033 0.033 0.033 0.032 0.033 0.033 0.033 0.987 0.179
id_38 0.033 0.033 0.033 0.032 0.033 0.033 0.033 0.987 0.179
DeviceType NA NA NA NA NA NA NA NA NA
DeviceInfo 0.040 0.040 0.040 0.039 0.041 0.040 0.040 0.367 0.191
id_33_1 0.145 0.145 0.145 0.149 0.145 0.145 0.145 0.175 0.078
id_33_2 0.145 0.145 0.145 0.149 0.145 0.145 0.145 0.175 0.078
id_30 id_31 id_32 id_34 id_35 id_36 id_37 id_38
id_02 0.179 0.946 0.179 0.177 0.993 0.993 0.993 0.993
id_03 0.048 0.146 0.048 0.047 0.160 0.160 0.160 0.160
id_04 0.048 0.146 0.048 0.047 0.160 0.160 0.160 0.160
id_05 0.113 0.743 0.113 0.111 0.742 0.742 0.742 0.742
id_06 0.113 0.743 0.113 0.111 0.742 0.742 0.742 0.742
id_07 0.148 0.034 0.148 0.135 0.033 0.033 0.033 0.033
id_08 0.148 0.034 0.148 0.135 0.033 0.033 0.033 0.033
id_09 0.085 0.166 0.085 0.082 0.179 0.179 0.179 0.179
id_10 0.085 0.166 0.085 0.082 0.179 0.179 0.179 0.179
id_11 0.180 0.951 0.180 0.176 0.987 0.987 0.987 0.987
id_13 -0.260 -0.059 -0.260 -0.263 -0.057 -0.057 -0.057 -0.057
id_14 0.978 0.182 0.978 0.952 0.182 0.182 0.182 0.182
id_15 0.178 0.939 0.178 0.180 1.000 1.000 1.000 1.000
id_16 0.333 0.478 0.334 0.325 0.492 0.492 0.492 0.492
id_17 0.143 0.867 0.143 0.141 0.866 0.866 0.866 0.866
id_18 0.135 0.120 0.136 0.135 0.122 0.122 0.122 0.122
id_19 0.143 0.863 0.143 0.140 0.861 0.861 0.861 0.861
id_20 0.143 0.849 0.143 0.141 0.848 0.848 0.848 0.848
id_21 0.148 0.034 0.148 0.135 0.033 0.033 0.033 0.033
id_22 0.149 0.035 0.149 0.135 0.033 0.033 0.033 0.033
id_23 0.149 0.035 0.149 0.135 0.033 0.033 0.033 0.033
id_24 0.153 0.033 0.153 0.140 0.032 0.032 0.032 0.032
id_25 0.149 0.034 0.149 0.136 0.033 0.033 0.033 0.033
id_26 0.148 0.034 0.148 0.135 0.033 0.033 0.033 0.033
id_27 0.149 0.035 0.149 0.135 0.033 0.033 0.033 0.033
id_28 0.180 0.951 0.180 0.176 0.987 0.987 0.987 0.987
id_29 0.085 0.166 0.085 0.082 0.179 0.179 0.179 0.179
id_30 1.000 0.178 1.000 0.972 0.178 0.178 0.178 0.178
id_31 0.178 1.000 0.178 0.160 0.939 0.939 0.939 0.939
id_32 1.000 0.178 1.000 0.972 0.178 0.178 0.178 0.178
id_34 0.972 0.160 0.972 1.000 0.180 0.180 0.180 0.180
id_35 0.178 0.939 0.178 0.180 1.000 1.000 1.000 1.000
id_36 0.178 0.939 0.178 0.180 1.000 1.000 1.000 1.000
id_37 0.178 0.939 0.178 0.180 1.000 1.000 1.000 1.000
id_38 0.178 0.939 0.178 0.180 1.000 1.000 1.000 1.000
DeviceType NA NA NA NA NA NA NA NA
DeviceInfo 0.418 0.368 0.417 0.391 0.362 0.362 0.362 0.362
id_33_1 0.970 0.174 0.970 0.944 0.173 0.173 0.173 0.173
id_33_2 0.970 0.174 0.970 0.944 0.173 0.173 0.173 0.173
DeviceType DeviceInfo id_33_1 id_33_2
id_02 NA 0.365 0.174 0.174
id_03 NA 0.172 0.042 0.042
id_04 NA 0.172 0.042 0.042
id_05 NA 0.272 0.112 0.112
id_06 NA 0.272 0.112 0.112
id_07 NA 0.040 0.145 0.145
id_08 NA 0.040 0.145 0.145
id_09 NA 0.191 0.078 0.078
id_10 NA 0.191 0.078 0.078
id_11 NA 0.367 0.175 0.175
id_13 NA -0.066 -0.242 -0.242
id_14 NA 0.408 0.949 0.949
id_15 NA 0.362 0.173 0.173
id_16 NA 0.633 0.324 0.324
id_17 NA 0.321 0.140 0.140
id_18 NA 0.078 0.133 0.133
id_19 NA 0.319 0.140 0.140
id_20 NA 0.314 0.140 0.140
id_21 NA 0.040 0.145 0.145
id_22 NA 0.040 0.145 0.145
id_23 NA 0.040 0.145 0.145
id_24 NA 0.039 0.149 0.149
id_25 NA 0.041 0.145 0.145
id_26 NA 0.040 0.145 0.145
id_27 NA 0.040 0.145 0.145
id_28 NA 0.367 0.175 0.175
id_29 NA 0.191 0.078 0.078
id_30 NA 0.418 0.970 0.970
id_31 NA 0.368 0.174 0.174
id_32 NA 0.417 0.970 0.970
id_34 NA 0.391 0.944 0.944
id_35 NA 0.362 0.173 0.173
id_36 NA 0.362 0.173 0.173
id_37 NA 0.362 0.173 0.173
id_38 NA 0.362 0.173 0.173
DeviceType 1 NA NA NA
DeviceInfo NA 1.000 0.404 0.404
id_33_1 NA 0.404 1.000 1.000
id_33_2 NA 0.404 1.000 1.000- NA 상관관계에서 보면 id_18가 가진 NA는 다른 변수와는 상관 없어 보임
- 다른 값으로 대체하거나 빼고 해야 할듯
3.
변수들간 NA 패턴
(na_pattern <- na_pattern_func(identityD, no_na_features)) pattern_cnt DeviceType id_15 id_35 id_36 id_37 id_38 id_02 id_11 id_28
1 2133 1 1 1 1 1 1 1 1 1
2 122 1 1 1 1 1 1 1 1 1
3 1 1 1 1 1 1 1 1 1 1
4 5 1 1 1 1 1 1 1 1 1
5 4 1 1 1 1 1 1 1 1 1
6 3 1 1 1 1 1 1 1 1 1
7 5 1 1 1 1 1 1 1 1 1
8 18344 1 1 1 1 1 1 1 1 1
9 358 1 1 1 1 1 1 1 1 1
10 54 1 1 1 1 1 1 1 1 1
11 13 1 1 1 1 1 1 1 1 1
12 3 1 1 1 1 1 1 1 1 1
13 30919 1 1 1 1 1 1 1 1 1
14 185 1 1 1 1 1 1 1 1 1
15 40 1 1 1 1 1 1 1 1 1
16 3913 1 1 1 1 1 1 1 1 1
17 45 1 1 1 1 1 1 1 1 1
18 5 1 1 1 1 1 1 1 1 1
19 1 1 1 1 1 1 1 1 1 1
20 4715 1 1 1 1 1 1 1 1 1
21 65 1 1 1 1 1 1 1 1 1
22 541 1 1 1 1 1 1 1 1 1
23 13 1 1 1 1 1 1 1 1 1
24 1 1 1 1 1 1 1 1 1 1
25 1005 1 1 1 1 1 1 1 1 1
26 3 1 1 1 1 1 1 1 1 1
27 100 1 1 1 1 1 1 1 1 1
28 1 1 1 1 1 1 1 1 1 1
29 98 1 1 1 1 1 1 1 1 1
30 1 1 1 1 1 1 1 1 1 1
31 3 1 1 1 1 1 1 1 1 1
32 1 1 1 1 1 1 1 1 1 1
33 2 1 1 1 1 1 1 1 1 1
34 1 1 1 1 1 1 1 1 1 1
35 11 1 1 1 1 1 1 1 1 1
36 35 1 1 1 1 1 1 1 1 1
37 3197 1 1 1 1 1 1 1 1 1
38 57 1 1 1 1 1 1 1 1 1
39 1 1 1 1 1 1 1 1 1 1
40 1 1 1 1 1 1 1 1 1 1
41 1 1 1 1 1 1 1 1 1 1
42 1 1 1 1 1 1 1 1 1 1
43 18075 1 1 1 1 1 1 1 1 1
44 224 1 1 1 1 1 1 1 1 1
45 33 1 1 1 1 1 1 1 1 1
46 3 1 1 1 1 1 1 1 1 1
47 29610 1 1 1 1 1 1 1 1 1
48 1 1 1 1 1 1 1 1 1 1
49 2 1 1 1 1 1 1 1 1 1
50 52 1 1 1 1 1 1 1 1 1
51 1 1 1 1 1 1 1 1 1 1
52 339 1 1 1 1 1 1 1 1 1
53 6 1 1 1 1 1 1 1 1 1
54 627 1 1 1 1 1 1 1 1 1
55 1 1 1 1 1 1 1 1 1 1
56 1 1 1 1 1 1 1 1 1 1
57 2 1 1 1 1 1 1 1 1 1
58 2 1 1 1 1 1 1 1 1 1
59 86 1 1 1 1 1 1 1 1 1
60 4 1 1 1 1 1 1 1 1 1
61 98 1 1 1 1 1 1 1 1 1
62 8 1 1 1 1 1 1 1 1 1
63 4 1 1 1 1 1 1 1 1 1
64 124 1 1 1 1 1 1 1 1 1
65 18 1 1 1 1 1 1 1 1 1
66 38 1 1 1 1 1 1 1 1 1
67 2 1 1 1 1 1 1 1 1 1
68 18 1 1 1 1 1 1 1 1 1
69 2 1 1 1 1 1 1 1 1 1
70 5 1 1 1 1 1 1 1 1 1
71 3 1 1 1 1 1 1 1 1 1
72 1 1 1 1 1 1 1 1 1 1
73 10 1 1 1 1 1 1 1 1 1
74 7 1 1 1 1 1 1 1 1 1
75 171 1 1 1 1 1 1 1 1 1
76 23 1 1 1 1 1 1 1 1 1
77 14 1 1 1 1 1 1 1 1 1
78 682 1 1 1 1 1 1 1 1 1
79 2 1 1 1 1 1 1 1 1 1
80 3 1 1 1 1 1 1 1 1 1
81 26 1 1 1 1 1 1 1 1 1
82 2 1 1 1 1 1 1 1 1 1
83 2 1 1 1 1 1 1 1 1 1
84 28 1 1 1 1 1 1 1 1 1
85 47 1 1 1 1 1 1 1 1 1
86 1 1 1 1 1 1 1 1 1 1
87 96 1 1 1 1 1 1 1 1 1
88 13 1 1 1 1 1 1 1 1 1
89 4 1 1 1 1 1 1 1 1 1
90 123 1 1 1 1 1 1 1 1 1
91 1 1 1 1 1 1 1 1 1 1
92 5 1 1 1 1 1 1 1 1 1
93 2 1 1 1 1 1 1 1 1 1
94 12 1 1 1 1 1 1 1 1 1
95 2 1 1 1 1 1 1 1 1 1
96 142 1 1 1 1 1 1 1 1 1
97 7 1 1 1 1 1 1 1 1 1
98 19 1 1 1 1 1 1 1 1 1
99 1 1 1 1 1 1 1 1 1 1
100 491 1 1 1 1 1 1 1 1 1
101 8 1 1 1 1 1 1 1 1 1
102 230 1 1 1 1 1 1 1 1 1
103 2 1 1 1 1 1 1 1 1 1
104 277 1 1 1 1 1 1 1 1 1
105 2 1 1 1 1 1 1 1 1 1
106 28 1 1 1 1 1 1 1 1 1
107 16 1 1 1 1 1 1 1 1 1
108 1 1 1 1 1 1 1 1 1 1
109 1 1 1 1 1 1 1 1 1 1
110 87 1 1 1 1 1 1 1 1 1
111 1 1 1 1 1 1 1 1 1 1
112 159 1 1 1 1 1 1 1 1 1
113 40 1 1 1 1 1 1 1 1 1
114 2 1 1 1 1 1 1 1 1 1
115 13200 1 1 1 1 1 1 1 1 1
116 23 1 1 1 1 1 1 1 1 1
117 22 1 1 1 1 1 1 1 1 1
118 32740 1 1 1 1 1 1 1 1 1
119 5 1 1 1 1 1 1 1 1 1
120 719 1 1 1 1 1 1 1 1 1
121 2 1 1 1 1 1 1 1 1 1
122 1 1 1 1 1 1 1 1 1 1
123 1847 1 1 1 1 1 1 1 1 1
124 2 1 1 1 1 1 1 1 1 1
125 10 1 1 1 1 1 1 1 1 1
126 19 1 1 1 1 1 1 1 1 1
127 2 1 1 1 1 1 1 1 1 1
128 9416 1 1 1 1 1 1 1 1 1
129 22 1 1 1 1 1 1 1 1 1
130 15 1 1 1 1 1 1 1 1 1
131 23088 1 1 1 1 1 1 1 1 1
132 640 1 1 1 1 1 1 1 1 1
133 1 1 1 1 1 1 1 1 1 1
134 1 1 1 1 1 1 1 1 1 1
135 295 1 1 1 1 1 1 1 1 1
136 5 1 1 1 1 1 1 1 1 1
137 2 1 1 1 1 1 1 1 1 1
138 4 1 1 1 1 1 1 1 1 1
139 259 1 1 1 1 1 1 1 1 1
140 9 1 1 1 1 1 1 1 1 1
141 10 1 1 1 1 1 1 1 1 1
142 1 1 1 1 1 1 1 1 1 1
143 16 1 1 1 1 1 1 1 1 1
144 9 1 1 1 1 1 1 1 1 1
145 4 1 1 1 1 1 1 1 1 1
146 1 1 1 1 1 1 1 1 1 1
147 3 1 1 1 1 1 1 1 1 1
148 2 1 1 1 1 1 1 1 1 1
149 12 1 1 1 1 1 1 1 1 1
150 1 1 1 1 1 1 1 1 1 1
151 3 1 1 1 1 1 1 1 1 1
152 1 1 1 1 1 1 1 1 1 1
153 2 1 1 1 1 1 1 1 1 1
154 3 1 1 1 1 1 1 1 1 1
155 55 1 1 1 1 1 1 1 1 1
156 92 1 1 1 1 1 1 1 1 1
157 1 1 1 1 1 1 1 1 1 1
158 122 1 1 1 1 1 1 1 1 1
159 1 1 1 1 1 1 1 1 1 1
160 5 1 1 1 1 1 1 1 1 1
161 1 1 1 1 1 1 1 1 1 1
162 1 1 1 1 1 1 1 1 1 1
163 5 1 1 1 1 1 1 1 1 1
164 3 1 1 1 1 1 1 1 1 1
165 2 1 1 1 1 1 1 1 1 1
166 3 1 1 1 1 1 1 1 1 1
167 3 1 1 1 1 1 1 1 1 1
168 1 1 1 1 1 1 1 1 1 1
169 95 1 1 1 1 1 1 1 1 1
170 6 1 1 1 1 1 1 1 1 1
171 11 1 1 1 1 1 1 1 1 1
172 517 1 1 1 1 1 1 1 1 1
173 4 1 1 1 1 1 1 1 1 1
174 19 1 1 1 1 1 1 1 1 1
175 3 1 1 1 1 1 1 1 1 1
176 3 1 1 1 1 1 1 1 1 1
177 1 1 1 1 1 1 1 1 1 1
178 53 1 1 1 1 1 1 1 1 1
179 9 1 1 1 1 1 1 1 1 1
180 1 1 1 1 1 1 1 1 1 1
181 311 1 1 1 1 1 1 1 1 1
182 11 1 1 1 1 1 1 1 1 1
183 91 1 1 1 1 1 1 1 1 1
184 1 1 1 1 1 1 1 1 1 1
185 88 1 1 1 1 1 1 1 1 1
186 2 1 1 1 1 1 1 1 1 1
187 3 1 1 1 1 1 1 1 1 1
188 17 1 1 1 1 1 1 1 1 1
189 137 1 1 1 1 1 1 1 1 1
190 254 1 1 1 1 1 1 1 1 1
191 4 1 1 1 1 1 1 1 1 1
192 2 1 1 1 1 1 1 1 1 1
193 1730 1 1 1 1 1 1 1 1 1
194 5 1 1 1 1 1 1 1 1 1
195 2 1 1 1 1 1 1 1 1 1
196 8365 1 1 1 1 1 1 1 1 1
197 155 1 1 1 1 1 1 1 1 1
198 517 1 1 1 1 1 1 1 1 1
199 5 1 1 1 1 1 1 1 1 1
200 1319 1 1 1 1 1 1 1 1 1
201 5 1 1 1 1 1 1 1 1 1
202 3 1 1 1 1 1 1 1 1 1
203 5760 1 1 1 1 1 1 1 1 1
204 27 1 1 1 1 1 1 1 1 1
205 1 1 1 1 1 1 1 1 1 1
206 1 1 1 1 1 1 1 1 1 1
207 158 1 1 1 1 1 1 1 1 1
208 13 1 1 1 1 1 1 1 1 1
209 5 1 1 1 1 1 1 1 1 1
210 212 1 1 1 1 1 1 1 1 1
211 2 1 1 1 1 1 1 1 1 1
212 9 1 1 1 1 1 1 1 1 1
213 25 1 1 1 1 1 1 1 1 1
214 1 1 1 1 1 1 1 1 1 1
215 244 1 1 1 1 1 1 1 1 1
216 4 1 1 1 1 1 1 1 1 1
217 443 1 1 1 1 1 1 1 1 1
218 1 1 1 1 1 1 1 1 1 1
219 10 1 1 1 1 1 1 1 1 1
220 17 1 1 1 1 1 1 1 1 1
221 1 1 1 1 1 1 1 1 1 1
222 2 1 1 1 1 1 1 1 1 1
223 2 1 1 1 1 1 1 1 1 1
224 7 1 1 1 1 1 1 1 1 1
225 9 1 1 1 1 1 1 1 1 1
226 2 1 1 1 1 1 1 1 1 1
227 1 1 1 1 1 1 1 1 1 1
228 4 1 1 1 1 1 1 1 1 1
229 4 1 1 1 1 1 1 1 1 1
230 5 1 1 1 1 1 1 1 1 1
231 1 1 1 1 1 1 1 1 1 1
232 8 1 1 1 1 1 1 1 1 1
233 499 1 1 1 1 1 1 1 1 1
234 258 1 1 1 1 1 1 1 1 1
235 3 1 1 1 1 1 1 1 1 1
236 441 1 1 1 1 1 1 1 1 1
237 1 1 1 1 1 1 1 1 1 1
238 1026 1 1 1 1 1 1 1 1 1
239 1 1 1 1 1 1 1 1 1 1
240 2 1 1 1 1 1 1 1 1 1
241 9 1 1 1 1 1 1 1 1 1
242 5 1 1 1 1 1 1 1 1 1
243 10 1 1 1 1 1 1 1 1 1
244 1 1 1 1 1 1 1 1 1 1
245 137 1 1 1 1 1 1 1 1 1
246 167 1 1 1 1 1 1 1 1 1
247 201 1 1 1 1 1 1 1 1 1
248 833 1 1 1 1 1 1 1 1 1
249 6863 1 1 1 1 1 1 1 1 1
250 10360 1 1 1 1 1 1 1 1 1
251 4551 1 1 1 1 1 1 1 1 1
252 8343 1 1 1 1 1 1 1 1 1
253 423 1 1 1 1 1 1 1 1 1
254 591 1 1 1 1 1 1 1 1 1
255 260 1 1 1 1 1 1 1 1 1
256 397 1 1 1 1 1 1 1 1 1
257 14 1 1 1 1 1 1 1 1 1
258 11 1 1 1 1 1 1 1 1 1
259 1 1 1 1 1 1 1 1 1 1
260 11 1 1 1 1 1 1 1 1 1
261 28 1 1 1 1 1 1 1 1 1
262 2 1 1 1 1 1 1 1 1 1
263 5 1 1 1 1 1 1 1 1 1
264 18 1 1 1 1 1 1 1 1 1
265 2841 1 1 1 1 1 1 1 1 1
266 5065 1 1 1 1 1 1 1 1 1
267 1 1 1 1 1 1 1 1 1 1
268 4 1 1 1 1 1 1 1 1 1
269 125 1 1 1 1 1 1 1 1 1
270 252 1 1 1 1 1 1 1 1 1
271 9 1 1 1 1 1 1 1 1 1
272 13 1 1 1 1 1 1 1 1 1
273 49 1 1 1 1 1 1 1 1 1
274 49 1 1 1 1 1 1 1 1 1
275 15 1 1 1 1 1 1 1 1 1
276 4 1 1 1 1 1 1 1 1 1
277 5 1 1 1 1 1 1 1 1 1
278 5 1 1 1 1 1 1 1 1 1
279 1 1 1 1 1 1 1 1 1 1
280 4 1 1 1 1 1 1 1 1 1
281 74 1 1 1 1 1 1 1 1 1
282 2 1 1 1 1 1 1 1 1 1
283 5 1 1 1 1 1 1 1 1 1
284 27 1 1 1 1 1 1 1 1 1
285 31 1 1 1 1 1 1 1 1 1
286 3 1 1 1 1 1 1 1 1 1
287 2 1 1 1 1 1 1 1 1 1
288 15 1 1 1 1 1 1 1 1 1
289 94 1 1 1 1 1 1 1 1 1
290 33 1 1 1 1 1 1 1 1 1
291 28 1 1 1 1 1 1 1 1 1
292 19 1 1 1 1 1 1 1 1 1
293 40 1 1 1 1 1 1 1 1 1
294 134 1 1 1 1 1 1 1 1 1
295 246 1 1 1 1 1 1 1 1 1
296 7 1 1 1 1 1 1 1 1 1
297 407 1 1 1 1 1 1 1 1 1
298 819 1 1 1 1 1 1 1 1 1
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299 1 1 0 0 0 0 0 0 0 0
300 1 0 0 0 0 0 0 0 0 0
301 0 1 0 0 0 0 0 0 0 0
302 0 0 0 0 0 0 0 0 0 0
303 1 1 0 0 0 0 0 0 0 0
304 1 0 0 0 0 0 0 0 0 0
305 0 0 0 0 0 0 0 0 0 0
306 1 0 0 0 0 0 0 0 0 0
307 1 1 0 0 0 0 0 0 0 0
308 1 0 0 0 0 0 0 0 0 0
309 0 1 0 0 0 0 0 0 0 0
310 0 0 0 0 0 0 0 0 0 0
311 0 1 0 0 0 0 0 0 0 0
312 0 0 0 0 0 0 0 0 0 0
313 0 1 0 0 0 0 0 0 0 0
314 0 0 0 0 0 0 0 0 0 0
315 0 1 0 0 0 0 0 0 0 0
316 0 1 0 0 0 0 0 0 0 0
317 1 1 0 0 0 0 0 0 0 0
318 1 0 0 0 0 0 0 0 0 0
319 1 0 0 0 0 0 0 0 0 0
320 1 1 0 0 0 0 0 0 0 0
321 1 0 0 0 0 0 0 0 0 0
322 0 0 0 0 0 0 0 0 0 0
323 0 0 0 0 0 0 0 0 0 0
324 0 1 0 0 0 0 0 0 0 0
325 0 0 0 0 0 0 0 0 0 0
326 1 1 0 0 0 0 0 0 0 0
327 1 0 0 0 0 0 0 0 0 0
328 0 1 0 0 0 0 0 0 0 0
329 0 0 0 0 0 0 0 0 0 0
330 0 1 0 0 0 0 0 0 0 0
331 0 0 0 0 0 0 0 0 0 0
332 1 1 0 0 0 0 0 0 0 0
333 1 0 0 0 0 0 0 0 0 0
334 0 1 0 0 0 0 0 0 0 0
335 0 0 0 0 0 0 0 0 0 0
336 0 1 0 0 0 0 0 0 0 0
337 0 0 0 0 0 0 0 0 0 0
338 1 0 1 1 1 1 1 1 1 1
339 1 0 1 1 1 1 1 1 1 1
340 1 0 1 1 1 1 0 0 1 1
341 1 0 0 0 0 0 0 0 0 0
342 0 0 1 1 1 1 1 1 1 1
343 0 0 0 0 0 0 0 0 0 0
344 1 0 1 1 1 1 1 1 1 1
345 1 0 0 0 0 0 0 0 0 0
346 0 0 0 0 0 0 0 0 0 0
347 0 1 0 0 0 0 0 0 0 0
348 0 0 1 1 1 1 1 1 1 1
349 0 0 1 1 1 1 1 1 1 1
350 0 0 1 1 1 1 0 0 1 1
351 0 0 0 0 0 0 0 0 0 0
352 0 0 1 1 1 1 1 1 1 1
353 0 0 0 0 0 0 0 0 0 0
354 1 0 0 0 0 0 0 0 0 0
355 0 0 1 1 1 1 1 1 1 1
356 1 0 0 0 0 0 0 0 0 0
357 0 0 0 0 0 0 0 0 0 0
358 0 1 0 0 0 0 0 0 0 0
359 0 0 1 1 1 1 1 1 1 1
360 0 0 1 1 1 1 1 1 1 1
361 0 0 0 0 0 0 0 0 0 0
362 0 0 1 1 1 1 1 1 1 1
363 0 0 0 0 0 0 0 0 0 0
364 1 0 0 0 0 0 0 0 0 0
365 0 0 0 0 0 0 0 0 0 0
366 1 0 1 1 1 1 1 1 1 1
367 1 0 1 1 1 1 1 1 1 1
368 1 0 0 0 0 0 0 0 0 0
369 0 0 0 0 0 0 0 0 0 0
370 0 0 1 1 1 1 1 1 1 1
371 0 0 1 1 1 1 1 1 1 1
372 0 0 0 0 0 0 0 0 0 0
373 1 0 1 1 1 0 1 1 1 1
374 1 0 0 0 0 0 0 0 0 0
375 1 0 0 0 0 0 0 0 0 0
376 0 0 0 0 0 0 0 0 0 0
377 1 0 1 1 1 1 1 1 1 1
378 1 0 1 1 1 1 0 0 1 1
379 1 0 0 0 0 0 0 0 0 0
380 0 0 0 0 0 0 0 0 0 0
381 0 0 0 0 0 0 0 0 0 0
382 1 0 0 0 0 0 0 0 0 0
383 0 0 0 0 0 0 0 0 0 0
384 1 0 0 0 0 0 0 0 0 0
385 0 0 0 0 0 0 0 0 0 0
386 0 1 0 0 0 0 0 0 0 0
387 0 0 1 1 1 1 1 1 1 1
388 0 0 1 1 1 1 1 1 1 1
389 0 0 0 0 0 0 0 0 0 0
390 0 0 0 0 0 0 0 0 0 0
391 0 0 0 0 0 0 0 0 0 0
392 0 0 0 0 0 0 0 0 0 0
393 0 0 0 0 0 0 0 0 0 0
394 0 1 0 0 0 0 0 0 0 0
395 0 0 0 0 0 0 0 0 0 0
396 1 0 0 0 0 0 0 0 0 0
397 0 0 0 0 0 0 0 0 0 0
398 1 0 0 0 0 0 0 0 0 0
399 1 0 0 0 0 0 0 0 0 0
400 0 1 0 0 0 0 0 0 0 0
401 0 0 0 0 0 0 0 0 0 0
402 0 1 0 0 0 0 0 0 0 0
403 0 0 0 0 0 0 0 0 0 0
404 0 0 0 0 0 0 0 0 0 0
405 1 0 0 0 0 0 0 0 0 0
406 0 0 0 0 0 0 0 0 0 0
407 1 0 0 0 0 0 0 0 0 0
408 0 0 0 0 0 0 0 0 0 0
409 1 0 0 0 0 0 0 0 0 0
410 0 0 0 0 0 0 0 0 0 0
411 0 0 0 0 0 0 0 0 0 0
412 0 0 0 0 0 0 0 0 0 0
413 0 0 0 0 0 0 0 0 0 0
414 1 1 1 1 1 1 1 1 0 1
415 1 1 0 0 0 0 0 0 0 0
416 1 0 1 1 1 1 1 1 1 1
417 1 0 0 0 0 0 0 0 0 0
418 0 1 0 0 0 0 0 0 0 0
419 0 0 1 1 1 1 1 1 1 1
420 0 0 0 0 0 0 0 0 0 0
421 0 1 1 1 1 1 1 1 0 1
422 0 1 0 0 0 0 0 0 0 0
423 0 0 1 1 1 1 1 1 1 1
424 0 0 0 0 0 0 0 0 0 0
425 1 0 0 0 0 0 0 0 0 0
426 0 1 1 1 1 1 1 1 0 1
427 1 1 0 0 0 0 0 0 0 0
428 1 0 0 0 0 0 0 0 0 0
429 0 1 0 0 0 0 0 0 0 0
430 0 0 0 0 0 0 0 0 0 0
431 0 1 0 0 0 0 0 0 0 0
432 0 0 0 0 0 0 0 0 0 0
433 1 1 0 0 0 0 0 0 0 0
434 1 1 0 0 0 0 0 0 0 0
435 1 0 0 0 0 0 0 0 0 0
436 0 1 0 0 0 0 0 0 0 0
437 0 1 0 0 0 0 0 0 0 0
438 0 0 0 0 0 0 0 0 0 0
439 0 1 0 0 0 0 0 0 0 0
440 0 1 0 0 0 0 0 0 0 0
441 0 1 0 0 0 0 0 0 0 0
442 0 0 0 0 0 0 0 0 0 0
443 1 1 0 0 0 0 0 0 0 0
444 1 0 0 0 0 0 0 0 0 0
445 0 1 0 0 0 0 0 0 0 0
446 1 0 1 1 1 1 1 1 1 1
447 1 0 0 0 0 0 0 0 0 0
448 0 0 1 1 1 1 1 1 1 1
449 1 1 1 1 1 1 1 1 1 0
450 1 1 0 0 0 0 0 0 0 0
451 1 0 0 0 0 0 0 0 0 0
452 0 1 0 0 0 0 0 0 0 0
453 1 1 0 0 0 0 0 0 0 0
454 0 1 1 1 1 1 1 1 1 0
455 0 1 0 0 0 0 0 0 0 0
456 0 0 1 1 1 1 1 1 1 0
457 0 0 0 0 0 0 0 0 0 0
458 0 1 0 0 0 0 0 0 0 0
459 0 1 1 1 1 1 1 1 1 0
460 0 1 0 0 0 0 0 0 0 0
461 1 0 0 0 0 0 0 0 0 0
462 1 1 1 1 1 1 1 1 1 0
463 1 1 0 0 0 0 0 0 0 0
464 1 0 0 0 0 0 0 0 0 0
465 0 1 0 0 0 0 0 0 0 0
466 0 0 0 0 0 0 0 0 0 0
467 0 1 0 0 0 0 0 0 0 0
468 0 0 0 0 0 0 0 0 0 0
469 1 0 0 0 0 0 0 0 0 0
470 0 1 0 0 0 0 0 0 0 0
471 0 0 0 0 0 0 0 0 0 0
472 0 1 0 0 0 0 0 0 0 0
473 0 1 0 0 0 0 0 0 0 0
474 0 0 0 0 0 0 0 0 0 0
475 1 0 0 0 0 0 0 0 0 0
476 0 1 0 0 0 0 0 0 0 0
477 0 0 0 0 0 0 0 0 0 0
478 0 0 0 0 0 0 0 0 0 0
479 1 1 0 0 0 0 0 0 0 0
480 1 0 0 0 0 0 0 0 0 0
481 0 0 0 0 0 0 0 0 0 0
482 0 1 0 0 0 0 0 0 0 0
483 0 0 0 0 0 0 0 0 0 0
484 1 1 0 0 0 0 0 0 0 0
485 1 0 0 0 0 0 0 0 0 0
486 1 0 1 1 1 1 1 1 1 1
487 0 1 1 1 1 1 1 1 1 1
488 0 0 1 1 1 1 1 1 1 1
489 0 0 1 1 1 1 1 1 1 1
490 1 0 1 1 1 1 1 1 1 1
491 1 0 1 1 1 1 1 1 1 1
492 1 0 1 1 1 1 1 1 1 1
493 1 0 1 1 1 1 1 1 1 0
494 1 0 1 1 1 1 0 0 1 1
495 0 0 1 1 1 1 1 1 1 1
496 0 0 1 1 1 1 1 1 1 1
497 0 0 1 1 1 1 0 0 1 1
498 1 0 1 1 1 1 1 1 1 1
499 0 0 1 1 1 1 1 1 1 1
500 1 0 1 1 1 1 1 1 1 1
501 0 0 1 1 1 1 1 1 1 1
502 0 0 1 1 1 1 1 1 1 1
503 0 0 1 1 1 1 1 1 1 1
504 0 0 1 1 1 1 1 1 1 0
505 0 0 1 1 1 1 1 1 0 1
506 0 0 1 1 1 1 0 0 1 1
507 0 0 1 1 1 0 1 1 1 1
508 0 0 1 1 1 1 1 1 1 1
509 0 0 1 1 1 1 1 1 1 1
510 1 0 1 1 1 1 1 1 1 1
511 1 0 1 1 1 1 1 1 1 1
512 0 0 1 1 1 1 1 1 1 1
513 0 0 1 1 1 1 1 1 1 1
514 1 0 1 1 1 1 1 1 1 1
515 0 0 1 1 1 1 1 1 1 1
516 1 0 1 1 1 1 1 1 1 1
517 1 0 1 1 1 1 1 1 1 1
518 0 0 1 1 1 1 1 1 1 1
519 0 0 1 1 1 1 1 1 1 1
520 0 0 1 1 1 1 1 1 1 1
521 0 0 1 1 1 1 1 1 1 1
522 0 0 1 1 1 1 1 1 1 1
523 0 0 1 1 1 1 1 1 1 1
524 1 0 1 1 1 1 1 1 1 1
525 0 0 1 1 1 1 1 1 1 1
526 0 0 1 1 1 1 1 1 1 1
527 1 0 1 1 1 1 1 1 1 1
528 1 0 1 1 1 1 1 1 1 1
529 1 0 1 1 1 1 1 1 1 0
530 0 0 1 1 1 1 1 1 1 1
531 0 0 1 1 1 1 1 1 1 1
532 0 0 1 1 1 1 1 1 1 1
533 0 0 1 1 1 1 1 1 1 1
534 1 0 1 1 1 1 1 1 1 1
535 1 0 1 1 1 1 1 1 1 1
536 0 0 1 1 1 1 1 1 1 1
537 1 0 1 1 1 1 1 1 1 1
538 1 0 1 1 1 1 1 1 1 1
539 1 0 1 1 1 1 1 1 1 1
540 0 0 1 1 1 1 1 1 1 1
541 0 0 1 1 1 1 1 1 1 1
542 1 0 1 1 1 1 1 1 1 1
543 1 0 1 1 1 1 1 1 1 1
544 1 0 1 1 1 1 1 1 1 0
545 0 0 1 1 1 1 1 1 1 1
546 0 0 1 1 1 1 1 1 1 1
547 0 0 1 1 1 1 1 1 1 1
548 0 0 1 1 1 1 1 1 1 0
549 0 0 1 1 1 1 1 1 0 1
550 0 0 1 1 1 1 1 1 1 1
551 0 0 1 1 1 1 1 1 1 1
552 0 0 1 1 1 1 1 1 1 1
553 0 0 1 1 1 1 1 1 1 1
554 0 0 1 1 1 1 1 1 1 1
555 0 0 1 1 1 1 1 1 1 1
556 0 0 1 1 1 1 1 1 1 1
557 0 0 1 1 1 1 1 1 1 1
558 0 0 1 1 1 1 1 1 1 1
559 0 0 1 1 1 1 1 1 1 1
560 0 0 1 1 1 1 1 1 1 1
561 0 0 1 1 1 1 1 1 0 1
562 0 0 1 1 1 1 1 1 1 1
563 0 0 1 1 1 1 1 1 1 1
564 0 0 1 1 1 1 1 1 1 1
565 0 0 1 1 1 1 1 1 1 0
566 1 1 0 0 0 0 0 0 0 0
567 1 0 0 0 0 0 0 0 0 0
568 1 1 0 0 0 0 0 0 0 0
569 1 0 0 0 0 0 0 0 0 0
570 0 1 0 0 0 0 0 0 0 0
571 0 0 0 0 0 0 0 0 0 0
572 0 1 0 0 0 0 0 0 0 0
573 0 0 0 0 0 0 0 0 0 0
574 1 1 0 0 0 0 0 0 0 0
575 0 1 0 0 0 0 0 0 0 0
576 1 1 0 0 0 0 0 0 0 0
577 1 0 0 0 0 0 0 0 0 0
578 0 1 0 0 0 0 0 0 0 0
579 0 0 0 0 0 0 0 0 0 0
580 1 1 0 0 0 0 0 0 0 0
581 1 0 0 0 0 0 0 0 0 0
582 1 1 0 0 0 0 0 0 0 0
583 1 0 1 1 1 1 1 1 1 1
584 1 0 0 0 0 0 0 0 0 0
585 0 0 1 1 1 1 1 1 1 1
586 0 0 0 0 0 0 0 0 0 0
587 1 1 0 0 0 0 0 0 0 0
588 1 0 0 0 0 0 0 0 0 0
589 0 1 0 0 0 0 0 0 0 0
590 1 1 1 1 1 1 1 1 1 1
591 1 1 0 0 0 0 0 0 0 0
592 1 0 0 0 0 0 0 0 0 0
593 0 1 0 0 0 0 0 0 0 0
594 0 0 0 0 0 0 0 0 0 0
595 0 1 0 0 0 0 0 0 0 0
596 0 0 0 0 0 0 0 0 0 0
597 0 1 0 0 0 0 0 0 0 0
598 0 0 0 0 0 0 0 0 0 0
599 0 0 0 0 0 0 0 0 0 0
600 0 1 0 0 0 0 0 0 0 0
601 0 0 0 0 0 0 0 0 0 0
602 1 1 0 0 0 0 0 0 0 0
603 1 0 0 0 0 0 0 0 0 0
604 1 1 0 0 0 0 0 0 0 0
605 1 0 0 0 0 0 0 0 0 0
606 0 1 0 0 0 0 0 0 0 0
607 0 0 0 0 0 0 0 0 0 0
608 0 1 0 0 0 0 0 0 0 0
609 0 0 0 0 0 0 0 0 0 0
610 0 1 0 0 0 0 0 0 0 0
611 0 0 0 0 0 0 0 0 0 0
612 1 1 0 0 0 0 0 0 0 0
613 1 0 0 0 0 0 0 0 0 0
614 0 1 0 0 0 0 0 0 0 0
615 0 0 0 0 0 0 0 0 0 0
616 0 0 0 0 0 0 0 0 0 0
617 0 0 0 0 0 0 0 0 0 0
618 0 0 0 0 0 0 0 0 0 0
619 1 0 0 0 0 0 0 0 0 0
620 1 0 0 0 0 0 0 0 0 0
621 1 0 0 0 0 0 0 0 0 0
622 1 0 0 0 0 0 0 0 0 0
623 1 0 0 0 0 0 0 0 0 0
624 1 0 0 0 0 0 0 0 0 0
625 0 0 0 0 0 0 0 0 0 0
626 1 0 0 0 0 0 0 0 0 0
627 0 0 0 0 0 0 0 0 0 0
628 0 1 0 0 0 0 0 0 0 0
629 0 0 0 0 0 0 0 0 0 0
630 0 1 0 0 0 0 0 0 0 0
631 0 0 0 0 0 0 0 0 0 0
632 0 0 0 0 0 0 0 0 0 0
633 0 0 0 0 0 0 0 0 0 0
634 1 0 0 0 0 0 0 0 0 0
635 0 0 0 0 0 0 0 0 0 0
636 0 0 0 0 0 0 0 0 0 0
637 0 0 0 0 0 0 0 0 0 0
638 0 0 0 0 0 0 0 0 0 0
639 0 0 0 0 0 0 0 0 0 0
640 153335 190152 275909 275909 275909 275922 275926 275926 275930 275969
id_24 na_cnt_in_pattern
1 1 0
2 0 1
3 1 1
4 0 2
5 1 2
6 0 3
7 0 2
8 0 9
9 1 1
10 0 2
11 1 2
12 0 4
13 0 10
14 1 2
15 0 3
16 0 11
17 1 3
18 0 4
19 1 4
20 0 12
21 1 2
22 0 11
23 1 3
24 0 4
25 0 12
26 1 4
27 0 13
28 1 5
29 0 14
30 0 11
31 1 4
32 0 5
33 0 13
34 1 6
35 0 12
36 0 13
37 1 5
38 0 6
39 1 6
40 0 7
41 1 7
42 0 7
43 0 14
44 1 6
45 0 7
46 1 7
47 0 15
48 1 5
49 0 15
50 1 7
51 0 8
52 0 16
53 1 8
54 0 17
55 0 15
56 1 9
57 0 18
58 1 10
59 1 1
60 0 2
61 0 10
62 1 2
63 0 3
64 0 11
65 1 3
66 0 12
67 1 4
68 0 13
69 0 4
70 0 12
71 0 14
72 0 15
73 1 5
74 0 6
75 0 14
76 1 6
77 0 7
78 0 15
79 1 7
80 0 8
81 0 16
82 1 8
83 0 9
84 0 17
85 1 6
86 0 7
87 0 15
88 1 7
89 0 8
90 0 16
91 1 8
92 0 17
93 0 18
94 1 10
95 0 11
96 0 19
97 1 11
98 0 12
99 0 14
100 0 20
101 1 5
102 0 14
103 1 6
104 0 15
105 1 7
106 0 16
107 0 17
108 0 18
109 1 10
110 0 19
111 1 11
112 0 20
113 1 6
114 0 7
115 0 15
116 1 7
117 0 8
118 0 16
119 1 8
120 0 17
121 1 9
122 0 10
123 0 18
124 0 18
125 0 19
126 1 11
127 0 12
128 0 20
129 1 12
130 0 13
131 0 21
132 1 1
133 0 2
134 1 2
135 0 10
136 1 2
137 0 3
138 1 3
139 0 11
140 1 3
141 0 12
142 0 5
143 0 13
144 1 3
145 0 12
146 0 13
147 0 15
148 0 11
149 0 12
150 0 13
151 0 14
152 1 5
153 0 14
154 0 15
155 1 6
156 0 15
157 1 7
158 0 16
159 1 8
160 0 17
161 0 18
162 1 7
163 0 16
164 0 17
165 0 19
166 0 11
167 1 6
168 0 7
169 0 15
170 1 7
171 0 8
172 0 16
173 0 17
174 0 18
175 0 17
176 1 11
177 0 14
178 0 20
179 0 13
180 0 15
181 0 21
182 1 6
183 0 15
184 1 7
185 0 16
186 0 17
187 0 18
188 1 11
189 0 20
190 0 21
191 1 7
192 0 8
193 0 16
194 1 8
195 0 9
196 0 17
197 0 18
198 0 19
199 1 12
200 0 21
201 1 13
202 0 14
203 0 22
204 1 3
205 0 4
206 0 5
207 0 12
208 1 4
209 0 5
210 0 13
211 0 14
212 0 15
213 1 6
214 0 7
215 0 15
216 1 7
217 0 16
218 1 8
219 0 17
220 0 18
221 1 4
222 0 14
223 1 7
224 0 16
225 0 17
226 0 12
227 0 20
228 0 17
229 0 18
230 0 20
231 1 12
232 0 21
233 0 18
234 0 19
235 1 12
236 0 21
237 1 14
238 0 22
239 0 14
240 0 16
241 0 17
242 0 18
243 0 19
244 0 13
245 0 21
246 0 22
247 0 19
248 0 20
249 0 22
250 0 23
251 0 10
252 0 11
253 0 12
254 0 13
255 0 12
256 0 13
257 0 14
258 0 15
259 0 11
260 0 14
261 0 15
262 0 16
263 0 13
264 0 14
265 0 15
266 0 16
267 0 15
268 0 16
269 0 17
270 0 18
271 0 19
272 0 20
273 0 11
274 0 12
275 0 13
276 0 14
277 0 13
278 0 14
279 0 15
280 0 15
281 0 16
282 0 17
283 0 18
284 0 16
285 0 17
286 0 18
287 0 19
288 0 20
289 0 21
290 0 15
291 0 16
292 0 20
293 0 21
294 0 16
295 0 17
296 0 19
297 0 21
298 0 22
299 0 11
300 0 12
301 0 13
302 0 14
303 0 13
304 0 14
305 0 16
306 0 13
307 0 15
308 0 16
309 0 17
310 0 18
311 0 16
312 0 17
313 0 18
314 0 19
315 0 17
316 0 20
317 0 12
318 0 13
319 0 15
320 0 16
321 0 17
322 0 19
323 0 18
324 0 21
325 0 22
326 0 16
327 0 17
328 0 18
329 0 19
330 0 21
331 0 22
332 0 17
333 0 18
334 0 19
335 0 20
336 0 22
337 0 23
338 1 3
339 0 4
340 0 6
341 0 12
342 1 5
343 0 14
344 1 5
345 0 14
346 0 16
347 0 16
348 1 8
349 0 9
350 0 11
351 0 17
352 1 10
353 0 19
354 0 13
355 1 6
356 0 17
357 0 19
358 0 17
359 1 9
360 0 10
361 0 18
362 1 13
363 0 22
364 0 17
365 0 22
366 1 9
367 0 10
368 0 18
369 0 20
370 1 14
371 0 15
372 0 23
373 1 5
374 0 13
375 0 15
376 0 18
377 0 10
378 0 12
379 0 18
380 0 23
381 0 23
382 0 19
383 0 21
384 0 22
385 0 24
386 0 17
387 1 9
388 0 10
389 0 18
390 0 20
391 0 19
392 0 24
393 0 24
394 0 24
395 0 25
396 0 13
397 0 15
398 0 15
399 0 17
400 0 17
401 0 18
402 0 19
403 0 20
404 0 22
405 0 18
406 0 23
407 0 19
408 0 24
409 0 14
410 0 16
411 0 19
412 0 24
413 0 25
414 1 2
415 0 10
416 1 2
417 0 11
418 0 12
419 1 4
420 0 13
421 1 7
422 0 15
423 1 7
424 0 16
425 0 16
426 1 9
427 0 16
428 0 17
429 0 18
430 0 19
431 0 21
432 0 22
433 0 16
434 0 17
435 0 18
436 0 19
437 0 22
438 0 23
439 0 22
440 0 20
441 0 23
442 0 24
443 0 11
444 0 12
445 0 16
446 1 4
447 0 13
448 1 6
449 1 2
450 0 10
451 0 11
452 0 12
453 0 12
454 1 7
455 0 15
456 0 9
457 0 16
458 0 17
459 1 8
460 0 20
461 0 16
462 1 8
463 0 16
464 0 17
465 0 18
466 0 19
467 0 21
468 0 22
469 0 18
470 0 22
471 0 23
472 0 19
473 0 23
474 0 24
475 0 12
476 0 16
477 0 18
478 0 25
479 0 11
480 0 12
481 0 14
482 0 16
483 0 17
484 0 17
485 0 18
486 1 4
487 1 5
488 1 6
489 1 9
490 1 5
491 1 6
492 0 7
493 1 7
494 0 9
495 1 8
496 0 9
497 0 11
498 1 8
499 1 10
500 1 10
501 1 12
502 1 11
503 0 12
504 1 12
505 1 12
506 0 14
507 0 13
508 1 13
509 1 15
510 1 7
511 0 8
512 1 9
513 0 10
514 1 9
515 1 11
516 1 11
517 0 12
518 1 13
519 0 14
520 1 12
521 1 14
522 1 16
523 0 17
524 1 11
525 1 13
526 1 16
527 1 12
528 0 13
529 1 13
530 1 14
531 0 15
532 1 17
533 0 18
534 1 7
535 1 9
536 1 12
537 1 8
538 1 12
539 0 13
540 0 18
541 0 18
542 1 13
543 0 14
544 1 14
545 0 16
546 1 18
547 0 19
548 1 19
549 1 19
550 1 9
551 1 12
552 1 14
553 1 10
554 1 13
555 0 18
556 1 17
557 1 15
558 0 16
559 1 18
560 0 19
561 1 19
562 0 17
563 1 19
564 0 20
565 1 20
566 0 10
567 0 11
568 0 12
569 0 13
570 0 15
571 0 16
572 0 17
573 0 18
574 0 11
575 0 20
576 0 16
577 0 17
578 0 21
579 0 22
580 0 11
581 0 12
582 0 16
583 1 8
584 0 17
585 1 13
586 0 22
587 0 16
588 0 17
589 0 21
590 1 8
591 0 17
592 0 18
593 0 19
594 0 20
595 0 22
596 0 23
597 0 16
598 0 19
599 0 23
600 0 23
601 0 24
602 0 11
603 0 12
604 0 13
605 0 14
606 0 16
607 0 17
608 0 18
609 0 19
610 0 22
611 0 23
612 0 12
613 0 15
614 0 17
615 0 18
616 0 24
617 0 19
618 0 19
619 0 16
620 0 18
621 0 17
622 0 18
623 0 22
624 0 22
625 0 27
626 0 23
627 0 29
628 0 24
629 0 25
630 0 25
631 0 26
632 0 30
633 0 31
634 0 18
635 0 29
636 0 29
637 0 30
638 0 31
639 0 37
640 276653 4471765- 각 행은 변수들 간 NA를 어떻게 가지고 있는지 패턴을 보여줌
- 맨 앞 pattern_cnt 변수 : 해당 패턴(행)이 전체 데이터에 몇 번 나타나는지
- 맨 뒤 na_cnt_in_pattern 변수 : 해당 패턴(행)에서 NA를 가진 변수 개수
- 그 외 변수 : 0이면 NA, 1이면 NA 아님
가장 많은 NA를 포함한 패턴부터 보면
tmp <- na_pattern
tmp <- tmp[-NROW(tmp),] %>% arrange(desc(na_cnt_in_pattern),desc(pattern_cnt))
(tmp <- rbind(na_pattern[NROW(na_pattern),],tmp)) pattern_cnt DeviceType id_15 id_35 id_36 id_37 id_38 id_02 id_11 id_28
640 NA 0 8178 8178 8178 8178 8178 8292 8384 8384
1 8178 1 0 0 0 0 0 0 0 0
2 15 1 1 1 1 1 1 1 0 0
3 1 1 1 1 1 1 1 0 0 0
4 19 1 1 1 1 1 1 1 0 0
5 1 1 1 1 1 1 1 0 1 1
6 110 1 1 1 1 1 1 0 1 1
7 6 1 1 1 1 1 1 1 1 1
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632 1 1 1 1 1 0 1 1 1 1
633 1 1 1 1 1 1 1 1 0 1
634 1 1 1 1 1 1 1 1 1 1
635 1 0 1 1 1 1 1 1 1 1
636 1 1 1 1 1 1 1 1 1 1
637 1 1 1 1 1 1 1 1 1 1
638 1 1 1 1 1 1 1 1 0 1
639 1 1 1 1 1 1 1 1 1 1
id_24 na_cnt_in_pattern
640 276653 4471765
1 0 37
2 0 31
3 0 31
4 0 30
5 0 30
6 0 29
7 0 29
8 0 29
9 0 27
10 0 26
11 0 25
12 0 25
13 0 25
14 0 25
15 0 25
16 0 24
17 0 24
18 0 24
19 0 24
20 0 24
21 0 24
22 0 24
23 0 24
24 0 24
25 0 24
26 0 24
27 0 23
28 0 23
29 0 23
30 0 23
31 0 23
32 0 23
33 0 23
34 0 23
35 0 23
36 0 23
37 0 23
38 0 23
39 0 23
40 0 23
41 0 23
42 0 22
43 0 22
44 0 22
45 0 22
46 0 22
47 0 22
48 0 22
49 0 22
50 0 22
51 0 22
52 0 22
53 0 22
54 0 22
55 0 22
56 0 22
57 0 22
58 0 22
59 0 22
60 0 22
61 0 22
62 0 22
63 0 22
64 0 22
65 0 21
66 0 21
67 0 21
68 0 21
69 0 21
70 0 21
71 0 21
72 0 21
73 0 21
74 0 21
75 0 21
76 0 21
77 0 21
78 0 21
79 0 21
80 0 21
81 0 21
82 0 20
83 0 20
84 0 20
85 0 20
86 0 20
87 0 20
88 0 20
89 0 20
90 0 20
91 0 20
92 0 20
93 0 20
94 1 20
95 0 20
96 0 20
97 0 20
98 0 20
99 0 20
100 0 20
101 0 20
102 0 20
103 0 20
104 0 19
105 0 19
106 0 19
107 0 19
108 0 19
109 0 19
110 0 19
111 1 19
112 0 19
113 0 19
114 0 19
115 0 19
116 0 19
117 0 19
118 0 19
119 0 19
120 1 19
121 0 19
122 0 19
123 0 19
124 0 19
125 0 19
126 0 19
127 0 19
128 0 19
129 0 19
130 0 19
131 0 19
132 0 19
133 0 19
134 0 19
135 1 19
136 1 19
137 0 19
138 0 19
139 0 19
140 0 19
141 0 18
142 0 18
143 0 18
144 0 18
145 0 18
146 0 18
147 0 18
148 0 18
149 0 18
150 0 18
151 1 18
152 0 18
153 1 18
154 0 18
155 0 18
156 0 18
157 0 18
158 0 18
159 0 18
160 0 18
161 0 18
162 0 18
163 0 18
164 0 18
165 0 18
166 0 18
167 0 18
168 0 18
169 0 18
170 0 18
171 0 18
172 0 18
173 0 18
174 0 18
175 0 18
176 0 18
177 0 18
178 0 18
179 0 18
180 0 18
181 0 18
182 0 18
183 0 18
184 0 18
185 0 18
186 0 18
187 0 18
188 0 17
189 0 17
190 0 17
191 0 17
192 0 17
193 0 17
194 0 17
195 0 17
196 0 17
197 1 17
198 0 17
199 0 17
200 0 17
201 0 17
202 0 17
203 0 17
204 0 17
205 0 17
206 0 17
207 0 17
208 0 17
209 0 17
210 0 17
211 0 17
212 0 17
213 0 17
214 0 17
215 0 17
216 0 17
217 0 17
218 0 17
219 0 17
220 0 17
221 0 17
222 0 17
223 0 17
224 0 17
225 0 17
226 0 17
227 0 17
228 0 17
229 0 17
230 0 17
231 0 17
232 0 17
233 0 17
234 0 17
235 1 17
236 0 17
237 0 17
238 0 16
239 0 16
240 0 16
241 0 16
242 0 16
243 0 16
244 0 16
245 0 16
246 0 16
247 0 16
248 0 16
249 0 16
250 0 16
251 0 16
252 0 16
253 0 16
254 0 16
255 0 16
256 0 16
257 0 16
258 0 16
259 0 16
260 0 16
261 0 16
262 0 16
263 0 16
264 0 16
265 0 16
266 1 16
267 0 16
268 0 16
269 0 16
270 0 16
271 0 16
272 0 16
273 0 16
274 0 16
275 0 16
276 1 16
277 0 16
278 0 16
279 0 16
280 0 16
281 0 16
282 0 16
283 0 16
284 0 16
285 0 16
286 0 16
287 0 15
288 0 15
289 0 15
290 0 15
291 0 15
292 0 15
293 0 15
294 0 15
295 0 15
296 0 15
297 0 15
298 0 15
299 0 15
300 0 15
301 1 15
302 0 15
303 0 15
304 0 15
305 0 15
306 0 15
307 0 15
308 0 15
309 0 15
310 0 15
311 0 15
312 1 15
313 0 15
314 0 15
315 0 15
316 0 15
317 0 15
318 0 15
319 0 15
320 0 15
321 0 15
322 0 15
323 0 14
324 0 14
325 0 14
326 0 14
327 0 14
328 0 14
329 0 14
330 0 14
331 0 14
332 0 14
333 0 14
334 0 14
335 0 14
336 1 14
337 0 14
338 0 14
339 0 14
340 0 14
341 0 14
342 0 14
343 0 14
344 1 14
345 0 14
346 0 14
347 0 14
348 0 14
349 0 14
350 1 14
351 0 14
352 1 14
353 0 14
354 0 14
355 1 14
356 1 14
357 0 13
358 0 13
359 0 13
360 0 13
361 0 13
362 0 13
363 0 13
364 1 13
365 0 13
366 0 13
367 0 13
368 1 13
369 0 13
370 0 13
371 0 13
372 0 13
373 0 13
374 1 13
375 0 13
376 0 13
377 1 13
378 0 13
379 0 13
380 0 13
381 1 13
382 1 13
383 0 13
384 0 13
385 0 13
386 0 13
387 0 13
388 0 13
389 1 13
390 0 13
391 0 13
392 0 13
393 1 13
394 1 13
395 0 13
396 0 12
397 0 12
398 0 12
399 0 12
400 0 12
401 0 12
402 0 12
403 1 12
404 0 12
405 0 12
406 1 12
407 1 12
408 0 12
409 0 12
410 0 12
411 0 12
412 0 12
413 0 12
414 0 12
415 1 12
416 0 12
417 0 12
418 1 12
419 1 12
420 0 12
421 0 12
422 1 12
423 1 12
424 0 12
425 0 12
426 0 12
427 0 12
428 0 12
429 0 12
430 1 12
431 0 12
432 0 12
433 0 12
434 1 12
435 1 12
436 1 12
437 0 12
438 0 12
439 0 11
440 0 11
441 0 11
442 0 11
443 1 11
444 0 11
445 0 11
446 0 11
447 0 11
448 1 11
449 1 11
450 0 11
451 1 11
452 1 11
453 0 11
454 0 11
455 0 11
456 1 11
457 0 11
458 1 11
459 0 11
460 0 11
461 1 11
462 0 11
463 1 11
464 0 11
465 0 11
466 0 11
467 0 11
468 0 11
469 0 11
470 0 10
471 0 10
472 0 10
473 0 10
474 0 10
475 1 10
476 0 10
477 1 10
478 0 10
479 1 10
480 0 10
481 0 10
482 1 10
483 0 10
484 1 10
485 1 10
486 1 10
487 0 10
488 0 10
489 0 10
490 0 9
491 1 9
492 1 9
493 0 9
494 1 9
495 1 9
496 0 9
497 1 9
498 0 9
499 0 9
500 1 9
501 1 9
502 1 9
503 1 9
504 1 9
505 0 9
506 0 9
507 1 9
508 1 8
509 1 8
510 1 8
511 0 8
512 0 8
513 1 8
514 1 8
515 1 8
516 0 8
517 1 8
518 0 8
519 1 8
520 0 8
521 1 8
522 0 8
523 0 8
524 1 8
525 1 8
526 1 8
527 1 8
528 1 8
529 1 8
530 1 7
531 1 7
532 0 7
533 1 7
534 0 7
535 1 7
536 0 7
537 1 7
538 1 7
539 1 7
540 1 7
541 1 7
542 1 7
543 1 7
544 1 7
545 1 7
546 0 7
547 1 7
548 1 7
549 0 7
550 1 7
551 0 7
552 0 7
553 1 7
554 1 7
555 0 7
556 1 7
557 0 7
558 1 7
559 1 6
560 1 6
561 0 6
562 1 6
563 1 6
564 1 6
565 1 6
566 1 6
567 1 6
568 0 6
569 1 6
570 0 6
571 1 6
572 1 6
573 1 6
574 1 6
575 1 6
576 1 6
577 1 5
578 1 5
579 1 5
580 1 5
581 0 5
582 1 5
583 1 5
584 0 5
585 1 5
586 0 5
587 1 5
588 0 5
589 1 5
590 1 5
591 1 5
592 1 4
593 1 4
594 0 4
595 0 4
596 0 4
597 1 4
598 1 4
599 1 4
600 0 4
601 1 4
602 0 4
603 0 4
604 1 4
605 1 4
606 1 4
607 1 3
608 0 3
609 1 3
610 1 3
611 1 3
612 1 3
613 1 3
614 1 3
615 0 3
616 1 3
617 0 3
618 0 3
619 1 2
620 1 2
621 0 2
622 1 2
623 1 2
624 0 2
625 0 2
626 1 2
627 1 2
628 0 2
629 1 2
630 1 2
631 0 2
632 1 2
633 1 2
634 1 1
635 1 1
636 0 1
637 1 1
638 1 1
639 1 0- NA처리해야 할 변수가 현재 29개 남은 상태에서 1개의 변수인 id_13만 제외하고 모두 NA인 패턴을 가진 데이터가 총 8178개임
- 심지어 id_15, id_35, id_36, id_37, id_38은 가진 NA가 모두 해당 패턴
- 8178개는 전체의 약 3%니까 걍 해당 패턴의 데이터 8178개 삭제 -> id_15, id_35, id_36, id_37, id_38은 NA 처리 자동 완료
na8187_features <- c('id_15','id_35','id_36','id_37','id_38')
identityD <- identityD %>% filter(!is.na(id_15))
NROW(identityD) # 277962 (286140-8187)[1] 2779624.
변수들간 NA 패턴 -> 가장 높은 빈도수를 가진 NA 패턴부터 보면
na_pattern <- na_pattern_func(identityD, c(no_na_features,na8187_features))
tmp <- na_pattern
tmp <- tmp[-NROW(tmp),] %>% arrange(desc(pattern_cnt),desc(na_cnt_in_pattern))
(tmp <- rbind(na_pattern[NROW(na_pattern),],tmp)) pattern_cnt DeviceType id_02 id_11 id_28 id_31 id_17 id_19 id_20 id_05
639 NA 0 114 206 206 1055 2627 2738 3068 6347
1 32740 1 1 1 1 1 1 1 1 1
2 30919 1 1 1 1 1 1 1 1 1
3 29610 1 1 1 1 1 1 1 1 1
4 23088 1 1 1 1 1 1 1 1 1
5 18344 1 1 1 1 1 1 1 1 1
6 18075 1 1 1 1 1 1 1 1 1
7 13200 1 1 1 1 1 1 1 1 1
8 10360 1 1 1 1 1 1 1 1 1
9 9416 1 1 1 1 1 1 1 1 1
10 8365 1 1 1 1 1 1 1 1 1
11 8343 1 1 1 1 1 1 1 1 1
12 6863 1 1 1 1 1 1 1 1 1
13 5760 1 1 1 1 1 1 1 1 1
14 5065 1 1 1 1 1 1 1 1 1
15 4715 1 1 1 1 1 1 1 1 1
16 4551 1 1 1 1 1 1 1 1 1
17 3913 1 1 1 1 1 1 1 1 1
18 3197 1 1 1 1 1 1 1 1 1
19 2841 1 1 1 1 1 1 1 1 1
20 2133 1 1 1 1 1 1 1 1 1
21 1847 1 1 1 1 1 1 1 1 1
22 1730 1 1 1 1 1 1 1 1 1
23 1401 1 1 1 1 1 1 1 1 0
24 1319 1 1 1 1 1 1 1 1 1
25 1026 1 1 1 1 1 1 1 1 1
26 1005 1 1 1 1 1 1 1 1 1
27 877 1 1 1 1 1 1 1 1 1
28 847 1 1 1 1 1 1 1 1 1
29 833 1 1 1 1 1 1 1 1 1
30 819 1 1 1 1 1 1 1 1 1
31 719 1 1 1 1 1 1 1 1 1
32 682 1 1 1 1 1 1 1 1 1
33 640 1 1 1 1 1 1 1 1 1
34 627 1 1 1 1 1 1 1 1 1
35 591 1 1 1 1 1 1 1 1 1
36 556 1 1 1 1 1 0 0 0 0
37 541 1 1 1 1 1 1 1 1 1
38 521 1 1 1 1 1 1 1 1 0
39 517 1 1 1 1 1 1 1 1 1
40 517 1 1 1 1 1 1 1 1 1
41 499 1 1 1 1 1 1 1 1 1
42 491 1 1 1 1 1 1 1 1 1
43 443 1 1 1 1 1 1 1 1 1
44 441 1 1 1 1 1 1 1 1 1
45 423 1 1 1 1 1 1 1 1 1
46 407 1 1 1 1 1 1 1 1 1
47 397 1 1 1 1 1 1 1 1 1
48 358 1 1 1 1 1 1 1 1 1
49 339 1 1 1 1 1 1 1 1 1
50 321 1 1 1 1 1 1 1 1 1
51 311 1 1 1 1 1 1 1 1 1
52 301 1 1 1 1 1 0 0 0 0
53 300 1 1 1 1 1 1 1 1 0
54 297 1 1 1 1 1 1 1 1 0
55 295 1 1 1 1 1 1 1 1 1
56 289 1 1 1 1 1 0 0 0 0
57 277 1 1 1 1 1 1 1 1 1
58 273 1 1 1 1 1 1 1 1 0
59 260 1 1 1 1 1 1 1 1 1
60 259 1 1 1 1 1 1 1 1 1
61 258 1 1 1 1 1 1 1 1 1
62 254 1 1 1 1 1 1 1 1 1
63 252 1 1 1 1 1 1 1 1 1
64 246 1 1 1 1 1 1 1 1 1
65 244 1 1 1 1 1 1 1 1 1
66 230 1 1 1 1 1 1 1 1 1
67 224 1 1 1 1 1 1 1 1 1
68 222 1 1 1 1 1 1 1 1 0
69 212 1 1 1 1 1 1 1 1 1
70 201 1 1 1 1 1 1 1 1 1
71 188 1 1 1 1 1 1 1 1 1
72 185 1 1 1 1 1 1 1 1 1
73 177 1 1 1 1 0 0 0 0 0
74 171 1 1 1 1 1 1 1 1 1
75 167 1 1 1 1 1 1 1 1 1
76 159 1 1 1 1 1 1 1 1 1
77 158 1 1 1 1 1 1 1 1 1
78 157 1 1 1 1 1 0 0 0 0
79 156 1 1 1 1 0 0 0 0 0
80 155 1 1 1 1 1 1 1 1 1
81 142 1 1 1 1 1 1 1 1 1
82 137 1 1 1 1 1 1 1 1 1
83 137 1 1 1 1 1 1 1 1 1
84 134 1 1 1 1 1 1 1 1 1
85 126 1 1 1 1 1 1 1 1 0
86 125 1 1 1 1 1 1 1 1 1
87 124 1 1 1 1 1 1 1 1 1
88 123 1 1 1 1 1 1 1 1 1
89 122 1 1 1 1 1 1 1 1 1
90 122 1 1 1 1 1 1 1 1 1
91 110 1 0 1 1 0 0 0 0 0
92 109 1 1 1 1 1 1 1 1 1
93 102 1 1 1 1 1 1 1 1 0
94 100 1 1 1 1 1 1 1 1 1
95 98 1 1 1 1 1 1 1 1 1
96 98 1 1 1 1 1 1 1 1 1
97 96 1 1 1 1 1 1 1 1 1
98 96 1 1 1 1 1 1 1 1 1
99 96 1 1 1 1 1 1 1 1 1
100 95 1 1 1 1 1 1 1 1 1
101 94 1 1 1 1 1 1 1 1 1
102 92 1 1 1 1 1 1 1 1 1
103 91 1 1 1 1 1 1 1 1 1
104 88 1 1 1 1 1 1 1 1 1
105 87 1 1 1 1 1 1 1 1 1
106 86 1 1 1 1 1 1 1 1 1
107 84 1 1 1 1 1 1 1 0 1
108 80 1 1 1 1 1 1 1 1 1
109 74 1 1 1 1 1 1 1 1 1
110 74 1 1 1 1 1 0 0 0 0
111 70 1 1 0 0 0 1 1 1 1
112 70 1 1 1 1 1 1 1 1 0
113 68 1 1 1 1 0 1 1 1 1
114 66 1 1 1 1 1 1 1 1 0
115 65 1 1 1 1 1 1 1 1 1
116 64 1 1 1 1 1 0 0 0 0
117 62 1 1 1 1 1 1 1 1 1
118 61 1 1 0 0 0 1 1 1 1
119 57 1 1 1 1 1 1 1 1 1
120 57 1 1 1 1 1 1 1 1 1
121 55 1 1 1 1 1 0 0 0 0
122 55 1 1 1 1 1 1 1 1 1
123 54 1 1 1 1 1 1 1 1 1
124 53 1 1 1 1 1 1 1 1 1
125 53 1 1 1 1 1 1 1 0 1
126 52 1 1 1 1 1 1 1 1 1
127 49 1 1 1 1 1 1 1 1 1
128 49 1 1 1 1 1 1 1 1 1
129 47 1 1 1 1 1 1 1 0 1
130 47 1 1 1 1 1 1 1 1 1
131 45 1 1 1 1 1 1 1 1 1
132 44 1 1 1 1 0 1 1 1 1
133 42 1 1 1 1 1 0 0 0 0
134 41 1 1 1 1 1 1 1 1 1
135 40 1 1 1 1 1 1 1 1 1
136 40 1 1 1 1 1 1 1 1 1
137 40 1 1 1 1 1 1 1 1 1
138 40 1 1 1 1 1 1 1 1 1
139 38 1 1 1 1 1 0 0 0 0
140 38 1 1 1 1 1 1 1 1 1
141 36 1 1 1 1 1 0 0 0 0
142 35 1 1 1 1 0 0 0 0 0
143 35 1 1 1 1 1 1 1 1 1
144 33 1 1 1 1 1 1 1 1 1
145 33 1 1 1 1 1 1 1 1 1
146 32 1 1 1 1 1 1 1 1 0
147 31 1 1 1 1 1 1 1 1 1
148 31 1 1 1 1 1 1 1 0 1
149 31 1 1 1 1 1 0 0 0 0
150 30 1 1 1 1 1 1 1 0 1
151 29 1 1 1 1 1 1 1 0 1
152 29 1 1 1 1 1 0 0 0 0
153 29 1 1 1 1 1 0 0 0 0
154 28 1 1 1 1 1 1 1 1 0
155 28 1 1 1 1 1 1 1 1 0
156 28 1 1 1 1 1 1 1 1 1
157 28 1 1 1 1 1 1 1 1 1
158 28 1 1 1 1 1 1 1 1 1
159 28 1 1 1 1 1 1 1 1 1
160 27 1 1 1 1 1 1 1 1 1
161 27 1 1 1 1 1 1 1 1 1
162 26 1 1 1 1 1 1 1 1 1
163 26 1 1 1 1 1 1 1 1 1
164 25 1 1 1 1 1 1 1 0 1
165 25 1 1 1 1 1 1 1 1 1
166 24 1 1 1 1 1 1 1 1 0
167 24 1 1 1 1 1 0 0 0 0
168 23 1 1 1 1 1 0 0 0 0
169 23 1 1 1 1 1 1 1 1 1
170 23 1 1 1 1 1 1 1 1 1
171 22 1 1 0 0 0 1 1 1 1
172 22 1 1 1 1 0 1 1 1 1
173 22 1 1 1 1 1 1 1 1 1
174 22 1 1 1 1 1 1 1 1 1
175 21 1 1 1 1 1 1 1 1 1
176 21 1 1 1 1 1 1 1 1 0
177 20 1 1 1 1 1 0 0 0 0
178 20 1 1 1 1 1 0 0 0 0
179 19 1 1 0 0 0 0 0 0 0
180 19 1 1 1 1 1 1 1 1 0
181 19 1 1 1 1 1 1 1 1 1
182 19 1 1 1 1 1 1 1 1 1
183 19 1 1 1 1 1 1 1 1 1
184 19 1 1 1 1 1 1 1 1 1
185 19 1 1 1 1 1 1 1 1 1
186 18 1 1 0 0 0 1 1 1 1
187 18 1 1 1 1 1 0 0 0 0
188 18 1 1 1 1 0 1 1 1 1
189 18 1 1 1 1 1 1 1 0 1
190 18 1 1 1 1 1 1 1 1 1
191 18 1 1 1 1 1 1 1 1 1
192 18 1 1 1 1 1 0 0 0 1
193 18 1 1 1 1 1 1 1 1 1
194 17 1 1 1 1 1 1 1 1 1
195 17 1 1 1 1 0 1 1 1 1
196 17 1 1 1 1 1 1 1 1 1
197 16 1 1 1 1 1 1 1 1 0
198 16 1 1 1 1 1 1 1 1 1
199 16 1 1 1 1 1 1 1 0 1
200 16 1 1 1 1 1 1 1 1 0
201 16 1 1 1 1 1 1 1 1 1
202 16 1 1 1 1 1 0 0 0 0
203 16 1 1 1 1 1 1 0 1 1
204 15 1 1 0 0 0 0 0 0 0
205 15 1 1 1 1 1 1 1 1 1
206 15 1 1 1 1 1 0 0 0 0
207 15 1 1 1 1 0 1 1 1 1
208 15 1 1 1 1 1 0 0 0 0
209 15 1 1 1 1 1 1 1 1 0
210 15 1 1 1 1 1 1 1 1 1
211 15 1 1 1 1 1 1 1 1 1
212 14 1 1 1 1 0 1 1 1 1
213 14 1 1 1 1 1 0 0 0 0
214 14 1 1 1 1 0 1 1 1 1
215 14 1 1 1 1 1 1 1 1 1
216 14 1 1 1 1 1 1 1 1 0
217 14 1 1 1 1 1 1 1 1 1
218 13 1 1 1 1 1 1 1 0 1
219 13 1 1 1 1 1 1 1 1 1
220 13 1 1 1 1 1 1 1 1 1
221 13 1 1 1 1 1 1 1 1 1
222 13 1 1 1 1 0 1 1 1 1
223 13 1 1 1 1 1 1 1 1 1
224 13 1 1 1 1 1 1 1 1 1
225 13 1 1 1 1 1 0 0 0 1
226 13 1 1 1 1 1 1 1 1 1
227 13 1 1 1 1 1 1 1 1 1
228 12 1 1 1 1 1 1 1 1 1
229 12 1 1 1 1 1 1 1 1 0
230 12 1 1 1 1 1 1 1 1 1
231 12 1 1 1 1 1 1 1 0 1
232 12 1 1 1 1 1 1 1 1 0
233 12 1 1 1 1 1 1 0 1 1
234 12 1 1 1 1 1 0 0 0 0
235 12 1 1 1 1 1 1 1 1 1
236 12 1 1 1 1 1 0 0 0 0
237 12 1 1 1 1 1 1 1 1 1
238 11 1 1 1 1 1 0 0 0 0
239 11 1 1 1 1 0 1 1 1 1
240 11 1 1 1 1 1 1 1 1 1
241 11 1 1 1 1 1 1 1 1 1
242 11 1 1 1 1 1 1 1 1 1
243 11 1 1 1 1 1 0 0 0 0
244 11 1 1 1 1 0 1 1 1 1
245 11 1 1 1 1 1 1 1 1 1
246 11 1 1 1 1 1 1 1 1 1
247 10 1 1 1 1 1 1 1 0 1
248 10 1 1 1 1 1 1 1 1 1
249 10 1 1 1 1 1 1 1 1 1
250 10 1 1 1 1 1 1 1 1 1
251 10 1 1 1 1 1 1 1 1 1
252 10 1 1 1 1 1 1 1 1 1
253 10 1 1 1 1 1 1 1 1 1
254 9 1 1 1 1 1 1 0 1 1
255 9 1 1 1 1 0 1 1 1 1
256 9 1 1 1 1 1 0 0 0 0
257 9 1 1 1 1 1 1 1 1 1
258 9 1 1 1 1 1 0 0 0 0
259 9 1 1 1 1 1 1 1 1 1
260 9 1 1 1 1 1 1 1 1 1
261 9 1 1 1 1 1 1 1 1 1
262 9 1 1 1 1 0 1 1 1 1
263 9 1 1 1 1 1 1 1 1 1
264 9 1 1 1 1 1 1 1 1 1
265 9 1 1 1 1 1 1 1 1 1
266 9 1 1 1 1 1 1 1 1 1
267 8 1 1 1 1 1 1 1 1 0
268 8 1 1 1 1 1 1 1 0 1
269 8 1 1 1 1 1 1 1 1 1
270 8 1 1 1 1 1 1 1 1 1
271 8 1 1 1 1 1 1 0 1 1
272 8 1 1 1 1 1 1 1 1 1
273 8 1 1 1 1 1 0 0 0 0
274 8 1 1 1 1 1 0 0 0 0
275 8 1 1 1 1 1 0 0 0 0
276 8 1 1 1 1 1 1 1 1 1
277 8 1 1 1 1 1 1 1 1 1
278 7 1 1 1 1 1 1 1 0 1
279 7 1 1 1 1 1 1 1 1 0
280 7 1 1 1 1 1 1 1 1 1
281 7 1 1 1 1 1 1 1 1 0
282 7 1 1 1 1 1 1 1 1 1
283 7 1 1 1 1 1 1 1 1 0
284 7 1 1 1 1 1 0 0 0 0
285 7 1 1 1 1 1 1 1 1 1
286 7 1 1 1 1 1 1 1 0 1
287 7 1 1 1 1 1 0 0 0 0
288 7 1 1 1 1 1 1 1 1 1
289 6 1 1 1 1 0 0 0 0 0
290 6 1 1 1 1 1 1 0 1 1
291 6 1 1 1 1 1 1 1 1 0
292 6 1 1 1 1 1 1 0 1 1
293 6 1 1 1 1 0 0 0 0 0
294 6 1 1 1 1 1 1 1 1 0
295 6 1 1 1 1 1 0 0 0 0
296 6 1 1 1 1 1 0 0 0 0
297 6 1 1 1 1 0 1 1 1 1
298 6 1 1 1 1 1 1 0 1 1
299 6 1 1 1 1 1 1 1 1 1
300 6 1 1 1 1 1 1 1 1 1
301 5 1 1 1 1 1 1 1 0 1
302 5 1 1 1 1 0 1 1 1 1
303 5 1 1 1 1 1 1 1 1 1
304 5 1 1 1 1 1 1 1 1 1
305 5 1 1 1 1 1 1 1 1 1
306 5 1 1 1 1 1 1 1 1 0
307 5 1 1 1 1 1 1 1 1 1
308 5 1 1 1 1 1 1 1 1 1
309 5 1 1 1 1 0 1 1 1 1
310 5 1 1 1 1 0 1 1 1 1
311 5 1 1 1 1 1 1 1 1 1
312 5 1 1 1 1 1 1 1 1 0
313 5 1 1 1 1 1 1 1 1 0
314 5 1 1 1 1 1 1 0 1 1
315 5 1 1 1 1 1 1 1 1 1
316 5 1 1 1 1 0 1 1 1 1
317 5 1 1 1 1 1 1 1 1 1
318 5 1 1 1 1 1 1 1 1 1
319 5 1 1 1 1 1 1 1 1 1
320 5 1 1 1 1 1 1 1 1 1
321 5 1 1 1 1 1 1 1 1 1
322 5 1 1 1 1 1 0 0 0 0
323 5 1 1 1 1 0 1 1 1 1
324 5 1 1 1 1 1 1 1 1 1
325 5 1 1 1 1 1 1 1 1 1
326 5 1 1 1 1 1 1 1 1 1
327 5 1 1 1 1 1 1 1 1 1
328 5 1 1 1 1 1 1 1 1 1
329 5 1 1 1 1 1 1 1 1 1
330 5 1 1 1 1 1 1 1 1 1
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431 0 0 0 0 0 0 16
432 0 0 0 0 0 0 16
433 1 1 1 1 1 1 16
434 1 1 1 1 1 0 16
435 0 0 0 0 0 0 15
436 1 1 1 1 1 1 15
437 0 0 0 0 0 0 14
438 0 0 0 0 0 0 14
439 0 0 0 0 0 0 14
440 0 0 0 0 0 0 13
441 0 0 0 0 0 0 13
442 1 1 1 1 1 1 13
443 1 1 1 1 1 1 13
444 1 1 1 1 1 0 13
445 1 1 1 1 1 0 12
446 1 1 1 1 1 0 12
447 0 0 0 0 0 0 12
448 0 0 0 0 0 0 12
449 1 1 1 1 1 0 11
450 0 0 0 0 0 0 11
451 1 1 1 1 1 1 11
452 1 1 1 1 1 1 10
453 1 1 1 1 1 0 10
454 1 1 1 1 1 1 10
455 1 1 1 1 1 1 10
456 1 1 1 1 1 0 9
457 1 1 1 1 1 1 9
458 1 1 1 1 1 0 9
459 1 1 1 1 1 0 9
460 1 1 1 1 1 1 9
461 1 1 1 1 1 1 9
462 1 1 1 1 1 1 8
463 1 1 1 1 1 0 8
464 1 1 1 1 0 1 8
465 1 1 1 1 1 0 8
466 1 1 1 1 1 1 7
467 1 1 1 1 1 1 7
468 1 1 1 1 1 0 7
469 1 1 1 1 1 1 7
470 1 1 1 1 0 1 7
471 1 1 1 1 1 1 6
472 1 1 1 1 1 1 6
473 1 1 1 1 1 1 4
474 1 1 1 1 1 0 4
475 1 1 1 1 1 0 3
476 1 1 1 1 1 1 2
477 0 0 0 0 0 0 31
478 0 0 0 0 0 0 30
479 0 0 0 0 0 0 29
480 0 0 0 0 0 0 27
481 0 0 0 0 0 0 25
482 0 0 0 0 0 0 24
483 0 0 0 0 0 0 24
484 0 0 0 0 0 0 23
485 0 0 0 0 0 0 23
486 0 0 0 0 0 0 23
487 0 0 0 0 0 0 22
488 0 0 0 0 0 0 22
489 0 0 0 0 0 0 22
490 0 0 0 0 0 0 22
491 0 0 0 0 0 0 22
492 0 0 0 0 0 0 21
493 0 0 0 0 0 0 21
494 0 0 0 0 0 0 20
495 0 0 0 0 0 0 20
496 0 0 0 0 0 0 20
497 0 0 0 0 0 0 20
498 0 0 0 0 0 0 20
499 0 0 0 0 0 0 19
500 0 0 0 0 0 0 19
501 0 0 0 0 0 0 19
502 0 0 0 0 0 0 19
503 0 0 0 0 0 0 19
504 1 1 1 0 1 1 19
505 1 1 1 0 1 1 19
506 0 0 0 0 0 0 19
507 0 0 0 0 0 0 19
508 0 0 0 0 0 0 19
509 0 0 0 0 0 0 19
510 0 0 0 0 0 0 18
511 0 0 0 0 0 0 18
512 0 0 0 0 0 0 18
513 0 0 0 0 0 0 18
514 0 0 0 0 0 0 18
515 0 0 0 0 0 0 18
516 1 1 1 1 1 0 18
517 1 1 1 1 1 0 18
518 1 1 1 1 1 0 18
519 0 0 0 0 0 0 18
520 0 0 0 0 0 0 18
521 0 0 0 0 0 0 18
522 0 0 0 0 0 0 18
523 0 0 0 0 0 0 17
524 0 0 0 0 0 0 17
525 0 0 0 0 0 0 17
526 0 0 0 0 0 0 17
527 0 0 0 0 0 0 17
528 0 0 0 0 0 0 17
529 0 0 0 0 0 0 17
530 1 1 1 1 1 1 17
531 0 0 0 0 0 0 17
532 0 0 0 0 0 0 17
533 0 0 0 0 0 0 16
534 0 0 0 0 0 0 16
535 0 0 0 0 0 0 16
536 0 0 0 0 0 0 16
537 0 0 0 0 0 0 16
538 0 0 0 0 0 0 16
539 0 0 0 0 0 0 16
540 0 0 0 0 0 0 16
541 0 0 0 0 0 0 16
542 0 0 0 0 0 0 15
543 0 0 0 0 0 0 15
544 1 0 0 1 1 0 15
545 0 0 0 0 0 0 15
546 0 0 0 0 0 0 15
547 0 0 0 0 0 0 15
548 0 0 0 0 0 0 15
549 1 1 1 1 1 0 15
550 0 0 0 0 0 0 15
551 0 0 0 0 0 0 15
552 1 0 0 1 1 0 14
553 1 0 0 1 1 0 14
554 1 1 1 1 0 1 14
555 0 0 0 0 0 0 14
556 1 1 1 1 1 1 14
557 0 0 0 0 0 0 14
558 1 0 0 1 1 0 14
559 1 1 1 1 1 1 14
560 1 1 1 1 1 1 14
561 0 0 0 0 0 0 13
562 0 0 0 0 0 0 13
563 0 0 0 0 0 0 13
564 1 1 1 1 1 0 13
565 0 0 0 0 0 0 13
566 1 1 1 1 1 1 13
567 0 0 0 0 0 0 13
568 0 0 0 0 0 0 13
569 0 1 1 1 1 0 13
570 1 1 1 1 0 1 13
571 1 1 1 1 1 1 13
572 0 0 0 0 0 0 13
573 1 1 1 1 1 1 12
574 1 0 0 1 1 0 12
575 0 0 0 0 0 0 12
576 0 0 0 0 0 0 12
577 1 1 1 1 1 1 12
578 1 1 1 1 0 1 12
579 1 1 1 0 1 1 12
580 0 0 0 0 0 0 12
581 0 0 0 0 0 0 12
582 0 0 0 0 0 0 11
583 1 1 1 1 1 1 11
584 0 0 0 0 0 0 11
585 1 0 0 1 1 0 11
586 0 0 0 0 0 0 11
587 1 0 0 1 1 0 11
588 0 0 0 0 0 0 11
589 0 0 0 0 0 0 11
590 1 1 1 1 1 1 10
591 1 1 1 1 1 0 10
592 1 1 1 1 1 0 10
593 1 1 1 1 1 0 10
594 1 1 1 1 1 1 9
595 1 1 1 1 1 1 9
596 1 1 1 0 1 1 9
597 1 1 1 1 0 0 9
598 1 0 0 1 1 0 9
599 1 1 1 1 1 1 9
600 1 1 1 1 1 0 8
601 1 1 1 1 1 1 8
602 1 1 1 1 1 1 8
603 1 1 1 1 1 1 8
604 1 1 1 1 0 1 8
605 1 1 1 1 1 1 8
606 1 1 1 1 1 1 8
607 1 1 1 0 1 0 7
608 1 1 1 0 0 1 7
609 0 1 1 1 1 0 7
610 1 1 1 1 1 0 7
611 1 1 1 1 1 1 7
612 1 1 1 1 1 1 7
613 1 1 1 1 1 0 7
614 1 1 1 1 1 1 7
615 1 1 1 1 1 0 7
616 1 1 1 1 1 1 7
617 1 1 1 1 1 1 6
618 1 1 1 1 0 1 6
619 1 1 1 1 1 1 6
620 1 1 1 1 1 1 5
621 1 1 1 1 1 0 5
622 1 1 1 1 1 1 5
623 1 1 1 1 1 0 5
624 1 1 1 1 1 1 5
625 1 1 1 0 1 0 5
626 1 1 1 1 1 1 5
627 0 1 1 1 1 1 5
628 1 1 1 1 1 1 5
629 1 1 1 1 0 1 4
630 1 1 1 1 1 0 4
631 1 1 1 1 1 0 4
632 1 1 1 1 1 1 4
633 1 1 1 1 1 1 4
634 1 1 1 1 1 1 4
635 1 1 1 1 1 0 2
636 0 1 1 1 1 1 2
637 1 1 1 0 1 1 2
638 1 1 1 0 1 1 1- 6개의 변수(id_14, id_34, id_32, id_30, id_33, id_18)가 모두 NA인 패턴이 32782(약 11%)개 있음
변수들의 NA 비율
(na_prop <- na_prop_func(identityD, c(no_na_features,na8187_features))) id_24 id_07 id_08 id_21 id_22 id_23
0.966 0.963 0.963 0.963 0.963 0.963
id_25 id_26 id_27 id_18 id_03 id_04
0.963 0.963 0.963 0.655 0.522 0.522
id_33_1 id_33_2 id_30 id_32 id_09 id_10
0.482 0.482 0.467 0.467 0.463 0.463
id_29 id_34 id_14 DeviceInfo id_13 id_16
0.463 0.460 0.455 0.159 0.103 0.082
id_05 id_06 id_20 id_19 id_17 id_31
0.023 0.023 0.011 0.010 0.009 0.004
id_11 id_28 id_02 DeviceType
0.001 0.001 0.000 0.000 각 변수들의 값 자체를 보자
na32782_features <- c('id_14','id_34','id_32','id_30','id_33','id_18')
tmp <- identityD[,na32782_features]
sapply(tmp,table)- 느낌이 뭔가 거래시 사용한 기기에 대한 정보 같음
- id_14는 시간인줄 알았는데 음…
- id_34는 html 크롤링 결과 같은데 무슨의미인지는…
- id_32는 무조건 메모리인듯 GB
- id_30은 무조건 기기명
- id_33은 무조건 해상도
- id_18은 뭐지? 화면 인치인가? 음….
구체적으로 더 전처리해야 하는데 일단 건너 뛰고
#tmp %>% filter(!(is.na(id_14),is.na(id_34),is.na(id_32),is.na(id_30),is.na(id_33),is.na(id_18)))5.
변수들의 NA 비율
#(na_prop <- na_prop_func(identityD, c(no_na_features,na8187_features,na32782_features)))